Deep foundation pit automatic monitoring system based on Internet of Things

Abstract

The invention discloses an automatic deep foundation pit monitoring system based on the Internet of Things, and relates to the technical field of deep foundation pit monitoring. A multi-parameter monitoring module is arranged to collect monitoring data of a deep foundation pit in the construction process in real time, and an overground analysis early warning module analyzes the real-time monitoring data of the deep foundation pit; the deep foundation pit monitoring unit arranges a plurality of small-sized processor devices according to the small-sized processor devices, in the process, when one small-sized processor device is successfully arranged, the IP address of the small-sized processor device is input into the sensors corresponding to the monitoring points of a plurality of parameters contained in the collaborative combination to be arranged, and a priority transmission strategy is set; and the collected monitoring values of the corresponding item parameters are transmitted to the small processor equipment preferentially, so that the speed of analyzing and early warning the key item parameters is accelerated, monitoring personnel can respond to key early warning in time, and stable proceeding of a project is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of deep foundation pit monitoring, and in particular to an automatic deep foundation pit monitoring system based on the Internet of Things. Background Art

[0002] Deep foundation pit refers to the project with excavation depth exceeding 5 meters (including 5 meters) or more than three basement floors (including three floors), or the project with depth not exceeding 5 meters but with particularly complex geological conditions, surrounding environment and underground pipelines;

[0003] Due to its great depth and complex environment, deep foundation pit projects may have an impact on the surrounding geological environment during and after construction. The soil and structure after completion are still gradually tending to a new equilibrium state, and there may be potential risks such as deformation and settlement. Therefore, various parameters of the deep foundation pit still need to be monitored after completion;

[0004] At present, the monitoring of various parameters of deep foundation pits is carried out by selecting several monitoring points in the deep foundation pits. Each monitoring point is equipped with a corresponding sensor to monitor and collect the monitoring values ​​of the corresponding parameters. The analysis and early warning processing of various parameters of deep foundation pits is carried out with the help of a trained foundation pit early warning model. This method can analyze and warn various parameters of deep foundation pits more accurately and quickly. This is only relative to the analysis process. In terms of transmission, all real-time monitoring values ​​are transmitted to the ground monitoring center. Due to the complex environmental characteristics of deep foundation pits, the transmission process may be delayed, which may cause delayed analysis of some key parameters, resulting in the inability of monitoring personnel to respond quickly and in a timely manner, resulting in unstable progress of the project.

[0005] In order to solve the above problems, the present invention proposes a solution. Summary of the invention

[0006] The purpose of the present invention is to provide an automatic monitoring system for deep foundation pits based on the Internet of Things. In order to solve the problem of transmitting all data to the ground monitoring center at the same time in the prior art, due to the complex environmental characteristics of the deep foundation pit, the transmission process may be delayed, which may cause delayed analysis of certain key parameters, resulting in the inability of monitoring personnel to respond quickly and in a timely manner, resulting in unstable progress of the project.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] The deep foundation pit automatic monitoring system based on the Internet of Things includes:

[0009] The ground analysis and early warning module is used to input the real-time monitoring data of the deep foundation pit into the deep foundation pit early warning model for analysis, and output the real-time analysis result data for storage;

[0010] The monitoring data of deep foundation pits include the monitoring values ​​of corresponding parameters monitored at several monitoring points in the deep foundation pits;

[0011] The analysis result data at a moment at least includes the analysis signal quantity of the deep foundation pit at that moment, and the analysis signal quantity of the deep foundation pit is used to indicate whether the deep foundation pit at that moment is abnormal. The analysis result data at that moment also includes the warning level and warning parameter data;

[0012] The warning level is used to indicate the risk level corresponding to the deep foundation pit abnormality at that moment. The warning parameter data includes several parameters that cause the deep foundation pit abnormality at that moment and their corresponding monitoring values ​​at that moment.

[0013] The ground analysis and warning module is also used to determine several parameter item combinations and remove duplicates according to the result analysis data with all analysis signal quantities of 1 stored at the current moment, and calculate the optimization evaluation index of each remaining parameter item combination based on the frequency of each remaining parameter item combination appearing in the result analysis data and the corresponding warning level. A parameter item combination includes several parameters carried in a result analysis data;

[0014] The optimization coordination module is used to determine a number of coordination combinations to be arranged and their center point coordinates according to the remaining number of parameter item combinations and their optimization weight evaluation indexes, and the deep foundation pit monitoring personnel arrange a small processor device based on the center point coordinates of each coordination combination to be arranged;

[0015] A small processor device stores a small collaborative warning model split from a deep foundation pit early warning model for analyzing several parameters of the corresponding collaborative combination to be deployed.

[0016] Furthermore, during the deep foundation pit construction process, the deep foundation pit monitoring personnel will determine the layout locations of several monitoring points and the corresponding parameters of each monitoring point for monitoring the deep foundation pit based on the excavation method of the deep foundation pit, the construction technology of the support structure, the geological conditions of the construction site, the surrounding buildings, transportation and pipelines of the deep foundation pit, etc.

[0017] Furthermore, it also includes a multi-parameter monitoring module for real-time collection of monitoring data of the deep foundation pit during the construction process.

[0018] Beneficial effects of the present invention:

[0019] (1) The present invention collects the monitoring data of the deep foundation pit in real time during the construction process by setting a multi-parameter monitoring module, and the ground analysis and early warning module analyzes the real-time monitoring data of the deep foundation pit. Based on the analysis result data with the analysis signal quantity of 1 contained therein, the optimization analysis unit obtains all the parameter item combinations, and calculates the optimization evaluation index of each remaining parameter item combination based on the frequency of each parameter item combination appearing in the result analysis data and the corresponding early warning level. The optimization coordination module determines the final coordinated combination to be deployed and the center point coordinates, and the deep foundation pit monitoring unit deploys several small processor devices based on them. Among them, the number and position of the small processors deployed and the selection of the corresponding received analysis parameters are based on the number of parameter item combinations with an early warning level of 3 that are contained in the result analysis data or are completely the same as the coordinated combination to be deployed corresponding to the small processor device. The small processors to be deployed are selected based on such conditions, thereby strengthening the necessity of their deployment.

[0020] (2) According to the present invention, each time a small processor device is successfully deployed by the deep foundation pit monitoring personnel, the IP address of the small processor device is input into the sensor of the monitoring point corresponding to the several parameters included in the collaborative combination to be deployed, and a priority transmission strategy is set to preferentially transmit the collected monitoring values ​​of the corresponding parameters to the small processor device. On the one hand, the rate of analyzing and warning of key parameters is accelerated, so that the monitoring personnel can respond to the key warnings in time to ensure the stable progress of the project. On the other hand, all the parameters of the deep foundation pit are processed separately, which reduces the network load and optimizes the transmission and analysis rate of all the parameters of the deep foundation pit. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below in conjunction with the accompanying drawings.

[0022] Figure 1 is a system block diagram of the present invention;

[0023] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] like Figure 1 , 2As shown in the figure, the deep foundation pit automatic monitoring system based on the Internet of Things includes a multi-parameter monitoring module, an optimization and coordination module, and a ground analysis and early warning module;

[0026] The multi-parameter monitoring module is used to monitor various parameters of the deep foundation pit during the construction process;

[0027] The various parameters of the deep foundation pit are finally selected by the deep foundation pit monitoring personnel after comprehensively considering the excavation method of the deep foundation pit, the construction technology of the support structure, the geological conditions of the construction site and the surrounding buildings of the deep foundation pit;

[0028] During the deep foundation pit construction process, the deep foundation pit monitoring personnel will determine the layout of several monitoring points and the corresponding parameters of each monitoring point for monitoring the deep foundation pit based on the excavation method of the deep foundation pit, the construction technology of the support structure, the geological conditions of the construction site, the surrounding buildings, transportation and pipelines of the deep foundation pit;

[0029] The multi-parameter monitoring module includes a plurality of parameter monitoring units, one parameter monitoring unit corresponds to a monitoring point arranged in the deep foundation pit, a corresponding sensor is installed in the monitoring point to monitor the corresponding item parameters of the deep foundation pit, and the parameter monitoring unit pre-stores the over-limit alarm threshold of the corresponding item parameters;

[0030] In this embodiment, the deep foundation pit used for monitoring has uniform geological conditions, no obvious geological variability, and a simple surrounding environment. Therefore, the corresponding monitoring parameters of each monitoring point arranged in the deep foundation pit are different;

[0031] After the monitoring and collection unit collects the monitoring value of the corresponding parameter in real time, it compares it with the over-limit alarm threshold of the corresponding parameter;

[0032] For the monitoring value of the corresponding parameter collected by a monitoring and collection unit at the current moment, compare it with the over-limit alarm threshold of the corresponding parameter. If the monitoring value of the corresponding parameter at the current moment is greater than its over-limit alarm threshold, a monitoring alarm instruction of the corresponding item is generated according to the monitoring value of the object parameter collected at the current moment, and the instruction is transmitted to the mobile device of the deep foundation pit monitoring personnel to monitor and alarm them. At the same time, after receiving the monitoring alarm instruction of the corresponding item, the deep foundation pit monitoring personnel can simultaneously view the collected monitoring value that exceeds its over-limit alarm threshold and the time when the monitoring value was collected;

[0033] Otherwise, no processing will be done for the time being;

[0034] The multi-parameter monitoring module generates real-time monitoring data of the deep foundation pit based on the monitoring values ​​of all parameters of the deep foundation pit acquired in real time by all monitoring and acquisition units, and transmits it to the ground analysis and early warning module;

[0035] The ground analysis and early warning module is used to conduct comprehensive analysis and early warning of various parameters of the deep foundation pit. The ground analysis and early warning module includes a ground analysis unit, a ground early warning unit and an optimization analysis unit. The ground analysis unit pre-stores a trained deep foundation pit early warning model.

[0036] After receiving the real-time deep foundation pit monitoring data, the ground analysis and early warning module transmits it to the ground analysis unit, which inputs it into the deep foundation pit early warning model. The deep foundation pit early warning model conducts a comprehensive analysis on the real-time monitoring values ​​of various parameters of the deep foundation pit, outputs the real-time analysis result data, and stores it.

[0037] The analysis result data at a moment at least includes the analysis signal quantity of the deep foundation pit at that moment. The analysis signal quantity of the deep foundation pit can only be a number 0 or 1. When the analysis signal quantity of the deep foundation pit at a moment is a number 0, it means that there is no abnormality in the deep foundation pit at that moment. Otherwise, it means that there is an abnormality in the deep foundation pit at that moment.

[0038] When the analysis signal quantity of the deep foundation pit at a certain moment is 1, the analysis result data at that moment also includes the warning level and warning parameter data;

[0039] The warning level is used to indicate the risk level corresponding to the deep foundation pit abnormality at that moment. In this embodiment, the warning level is divided into 1, 2, and 3 levels. The smaller the number, the greater the risk level.

[0040] The early warning parameter data includes several parameters that cause the deep foundation pit abnormality at that moment and their corresponding monitoring values ​​at that moment;

[0041] The above-ground analysis unit transmits the real-time analysis result data to the above-ground early warning unit. After receiving the transmitted analysis result data, the above-ground early warning unit selects whether to send the analysis result data to the mobile device of the deep foundation pit monitoring personnel based on the analysis signal quantity carried therein;

[0042] The optimization analysis unit analyzes and optimizes the result analysis data with the analysis signal quantity of 1 stored in the ground warning unit at the current moment. The specific steps are as follows:

[0043] S11: traverse all result analysis data whose analysis signal quantity is 1 stored in the ground warning unit at the current moment to obtain a plurality of parameter item combinations;

[0044] One parameter item combination corresponds to a result analysis data with an analysis signal quantity of 1 stored in the ground warning unit at the current moment, and one parameter item combination includes several parameters carried in the corresponding result analysis data;

[0045] S12: removing duplicates from the obtained multiple parameter item combinations, and marking all remaining parameter item combinations after the removal of duplicates as A1, A2, ..., Aa, where a≥1;

[0046] The deduplication rule is as follows: among these multiple parameter item combinations, only one parameter item combination that is completely consistent is retained. Here, completely consistent means that the multiple parameters contained in the parameter item combination are completely consistent in content and quantity;

[0047] S13: from the result analysis data of all the analysis signal quantities stored in the ground warning unit at the current moment, obtain the result analysis data of several parameters carried therein that are completely consistent with the several parameters contained in the parameter item combination A1 or contain all the parameters in the parameter item combination A1, extract the number of the warning levels of 1, 2, and 3, and mark them as B1, B2, and B3 respectively;

[0048] S14: Utilize the formula Calculate and obtain the optimized evaluation index C1 of the parameter item combination A1, wherein PB is the total number of result analysis data with the analysis signal quantity of 1 stored in the ground warning unit at the current moment, and α1 and α2 are respectively the preset first and second adjustment factors;

[0049] S15: Calculate and obtain the optimization weight indexes C1, C2, ..., Ca of the parameter item combinations A1, A2, ..., Aa respectively according to S11 to S14;

[0050] The optimization analysis unit transmits all parameter item combinations and their optimization evaluation indexes to the optimization coordination module;

[0051] An optimization collaboration module is used to collaboratively optimize the transmission path of the corresponding parameter monitoring values ​​collected at each monitoring point in the deep foundation pit, wherein the optimization collaboration module pre-stores the position data of all monitoring points arranged in the deep foundation pit and the corresponding parameters monitored by the monitoring points;

[0052] The location data of a monitoring point refers to the latitude and longitude coordinates of the sensor installed there;

[0053] After receiving all the transmitted parameter item combinations and their optimized weight indexes, the optimization coordination module coordinates and optimizes the transmission path of the monitoring values ​​of the corresponding item parameters collected at each monitoring point in the current deep foundation pit according to the preset optimization coordination rules. The specific optimization coordination rules are as follows:

[0054] S21: recalibrate all received parameter item combinations in descending order according to their corresponding optimization weight indexes as D1, D2, ..., Da;

[0055] S22: Obtain all item parameters included in the parameter item combination D1, and mark them as E1, E2, ..., Ee respectively, where e ≥ 1;

[0056] S23: Take (0, 0) as the coordinate origin to establish an xy-plane rectangular coordinate system. According to the position data of the monitoring points corresponding to the parameters E1, E2, ..., Ee stored in the optimization collaboration module, map them in the xy-plane rectangular coordinate system. Then, for each parameter, there is a coordinate point in the xy-plane rectangular coordinate system corresponding to the monitoring point, which can be represented as a position;

[0057] S24: Based on the coordinate points of the monitoring points corresponding to the parameters E1, E2, ..., Ee in the xy-plane rectangular coordinate system, calculate and obtain the rectangle with the smallest area that can cover these coordinate points, and take it as the minimum bounding rectangle of the parameter item combination D1. Then, find the center point coordinates of this rectangle and take them as the center point coordinates of the parameter item combination D1;

[0058] S25: Calculate the straight-line distances between the coordinate points of the monitoring points corresponding to the parameters E1, E2, ..., Ee in the xy-plane rectangular coordinate system and the center point coordinates of the parameter item combination D1 in sequence, and mark them as F1, F2, ..., Fe respectively;

[0059] S26: Use the formula to calculate and obtain the parameter G1, where G1 represents the residual of the straight-line distance between the monitoring points corresponding to E1, E2, ..., Ee and the center point of the parameter item combination D1. Compare the magnitudes of G1 and G. At this time, F is the average value of Fg, and G is the residual comparison threshold for the distance of the parameters in the preset parameter item combination D1 from the center point;

[0060] If G1 ≥ G, then delete the corresponding Fg in descending order of |Fg - F| and calculate the residual G1 of the remaining Fg. Compare the magnitudes of G1 and G again until G1 < G. Then, re-calibrate the average value of the remaining Fg participating in the calculation of G1 as the average layout distance H1 of the parameter item combination D1;

[0061] S27: Compare the magnitudes of H1 and H. If H1 ≥ H, do nothing. Otherwise, select the parameter item combination D1 as the to-be-laid-out collaboration combination, where H is the preset signal transmission distance threshold;

[0062] S28: According to S21 to S27, calculate and obtain the average layout distances H1, H2, ..., Ha of the parameter item combinations D1, D2, ..., Da in sequence. Then, compare the magnitudes of the average layout distances H1, H2, ..., Ha and H in sequence. Based on the comparison results, obtain all the selected to-be-laid-out collaboration combinations, and mark the total number of all the selected to-be-laid-out collaboration combinations as I1;

[0063] S29: Compare the magnitudes of I1 and I. If I1 ≥ I, extract the first I to-be-deployed collaborative combinations in the order of D1, D2, ..., Da, and transmit these I to-be-deployed collaborative combinations and their central point coordinates to the mobile device of the deep foundation pit monitoring personnel. If I1 < I, transmit all the to-be-deployed collaborative combinations and their central point coordinates to the mobile device of the deep foundation pit monitoring personnel. I is the preset threshold for the number of small processor devices to be deployed.

[0064] The optimization collaboration module transmits all the selected to-be-deployed collaborative combinations and their central point coordinates to the mobile device of the deep foundation pit monitoring personnel. Based on the central point coordinates of each to-be-deployed collaborative combination, the deep foundation pit monitoring personnel deploy a small processor device at the corresponding position in the deep foundation pit to receive and analyze the monitoring values of several parameters of the corresponding to-be-deployed collaborative combination.

[0065] For each successfully deployed small processor device by the deep foundation pit monitoring personnel, input the Ip of the small processor device among the sensors at the monitoring points of several parameters included in the to-be-deployed collaborative combination, and set the priority transmission strategy to preferentially transmit the monitoring values of the corresponding parameters collected to this small processor device.

[0066] After the deployment is completed, the optimization system module transmits all the to-be-deployed collaborative combinations and the ip addresses of the corresponding deployed small processor devices to the ground analysis unit.

[0067] After the ground analysis unit receives the transmitted all to-be-deployed collaborative combinations and the ip addresses of the corresponding deployed small processor devices, first extract from the deep foundation pit warning model the collaborative parameter warning sub-models for analyzing several parameters included in each to-be-deployed collaborative combination. One to-be-deployed collaborative combination corresponds to one collaborative parameter warning sub-model.

[0068] Then, according to the ip address of the small processor device corresponding to each to-be-deployed collaborative combination, transmit the corresponding collaborative parameter warning sub-model to the corresponding small processor device.

[0069] After all the collaborative parameter warning sub-models are transmitted, the ground analysis unit generates a start command and transmits it to the corresponding sensors and all small processor devices.

[0070] After the corresponding sensor receives the transmitted start command, according to the ip address of the small processor device stored therein, preferentially transmits the monitoring values of the corresponding parameters collected to this small processor device.

[0071] After receiving the transmission start instruction, the small processor device receives the monitoring values ​​of the corresponding parameters and inputs them into the small collaborative parameter early warning model stored therein for analysis, and obtains the analysis result data, and selects whether to send the analysis result data to the mobile device of the deep foundation pit monitoring personnel based on the analysis signal quantity carried therein;

[0072] In the description of the specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0073] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

[0074] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The deep foundation pit automatic monitoring system based on the Internet of Things is characterized by: include: The ground analysis and early warning module is used to input the real-time monitoring data of the deep foundation pit into the deep foundation pit early warning model for analysis, and output the real-time analysis result data for storage; The monitoring data of deep foundation pits include the monitoring values ​​of corresponding parameters monitored at several monitoring points in the deep foundation pits; The analysis result data at a moment at least includes the analysis signal quantity of the deep foundation pit at that moment, and the analysis signal quantity of the deep foundation pit is used to indicate whether the deep foundation pit at that moment is abnormal. The analysis result data at that moment also includes the warning level and warning parameter data; The warning level is used to indicate the risk level corresponding to the deep foundation pit abnormality at that moment. The warning parameter data includes several parameters that cause the deep foundation pit abnormality at that moment and their corresponding monitoring values ​​at that moment. The ground analysis and warning module is also used to determine several parameter item combinations and remove duplicates according to the result analysis data with all analysis signal quantities of 1 stored at the current moment, and calculate the optimization evaluation index of each remaining parameter item combination based on the frequency of each remaining parameter item combination appearing in the result analysis data and the corresponding warning level. A parameter item combination includes several parameters carried in a result analysis data; The optimization coordination module is used to determine a number of coordination combinations to be arranged and their center point coordinates according to the remaining number of parameter item combinations and their optimization weight evaluation indexes, and the deep foundation pit monitoring personnel arrange a small processor device based on the center point coordinates of each coordination combination to be arranged; A small processor device stores a small collaborative warning model split from a deep foundation pit early warning model for analyzing several parameters of the corresponding collaborative combination to be deployed.

2. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 1 is characterized in that: During the deep foundation pit construction process, the deep foundation pit monitoring personnel will determine the layout of several monitoring points and the corresponding parameters of each monitoring point for monitoring the deep foundation pit based on the excavation method of the deep foundation pit, the construction technology of the support structure, the geological conditions of the construction site, the surrounding buildings, transportation and pipelines of the deep foundation pit.

3. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 1 is characterized in that: It also includes a multi-parameter monitoring module for real-time collection of monitoring data of deep foundation pits during construction.

4. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 1 is characterized in that: The analysis signal quantity of the deep foundation pit can only be the number 0 or 1. When the analysis signal quantity of the deep foundation pit is the number 0 at a certain moment, it means that there is no abnormality in the deep foundation pit at that moment. Otherwise, it means that there is an abnormality in the deep foundation pit at that moment.

5. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 1 is characterized in that: The warning levels are divided into 1, 2, and 3. The smaller the number, the greater the risk.

6. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 1 is characterized in that: The specific steps for the ground analysis and early warning module to calculate the optimized weight evaluation index of each remaining parameter item combination are as follows: S11: traverse all result analysis data with the analysis signal quantity of 1 stored in the ground warning unit at the current moment to obtain a plurality of parameter item combinations; S12: removing duplicates from the obtained parameter item combinations, and marking all remaining parameter item combinations after the removal of duplicates as A1, A2, ..., Aa, where a≥1; S13: Obtain, from all the result analysis data with the analysis signal quantity stored in the ground warning unit at the current moment being 1, the result analysis data whose carried several parameters are exactly the same as several parameters included in the parameter item combination A1 or contain all items of parameters in the parameter item combination A1, and extract the quantities with the warning levels being 1, 2, and 3 respectively, and mark them as B1, B2, and B3; S14: Utilize the formula Calculate and obtain the optimized evaluation index C1 of the parameter item combination A1, wherein PB is the total number of result analysis data with the analysis signal quantity of 1 stored in the ground warning unit at the current moment, and α1 and α2 are respectively the preset first and second adjustment factors; S15: Calculate and obtain the optimized evaluation weight indexes C1, C2,..., Ca of the parameter item combinations A1, A2,..., Aa respectively according to S11 to S14.

7. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 1 is characterized in that: The specific steps for the optimization cooperation module to determine several to-be-arranged cooperation combinations and their center point coordinates are as follows: S21: Re-label all the received parameter item combinations as D1, D2,..., Da in descending order according to their corresponding optimized evaluation weight indexes; S22: Obtain all items of parameters included in the parameter item combination D1, and mark them as E1, E2,..., Ee respectively, where e≥1; S23: Take (0,0) as the coordinate origin to establish an xy plane rectangular coordinate system. According to the position data of the monitoring points corresponding to the parameters E1, E2,..., Ee stored in the optimization cooperation module, map them in the xy plane rectangular coordinate system. Then, based on each parameter, there is a coordinate point in the xy plane rectangular coordinate system corresponding to the monitoring point, which can be represented as a position; S24: Based on the coordinate points of the monitoring points corresponding to the parameters E1, E2,..., Ee in the xy plane rectangular coordinate system, calculate and obtain the rectangle with the minimum area that can cover these coordinate points, take it as the minimum enclosing rectangle of the parameter item combination D1, and find the center point coordinate of this rectangle, and take it as the center point coordinate of the parameter item combination D1; S25: Calculate and obtain the straight-line distances between the coordinate points of the monitoring points corresponding to the parameters E1, E2,..., Ee in the xy plane rectangular coordinate system and the center point coordinate of the parameter item combination D1, and mark them as F1, F2,..., Fe respectively; S26: Utilize the formula Calculate and obtain parameter G1, G1 represents the residual of the straight-line distance between the corresponding monitoring points of E1, E2, ..., Ee and the center point of parameter item combination D1, compare G1 and G, at this time, F is the average value of Fg, and G is the residual comparison threshold of the distance between the parameter and the center point in the preset parameter item combination D1; If G1≥G, then delete the corresponding Fg in descending order of |Fg - F| and calculate the residual G1 of the remaining Fg, and compare the sizes of G1 and G again until G1 < G. Then, re-label the mean value of the remaining Fg participating in the calculation of G1 as the average layout distance H1 of the parameter item combination D1; S27: Compare the sizes of H1 and H. If H1≥H, do nothing. Otherwise, select the parameter item combination D1 as the to-be-arranged cooperation combination, where H is the preset signal transmission distance threshold; S28: Calculate and obtain the average layout distances H1, H2,..., Ha of the parameter item combinations D1, D2,..., Da respectively according to S21 to S27. Then, compare the sizes of the average layout distances H1, H2,..., Ha and H in turn. Based on the comparison results, obtain all the selected to-be-arranged cooperation combinations, and mark the total number of all the selected to-be-arranged cooperation combinations as I1; S29: Compare the magnitudes of I1 and I. If I1 ≥ I, extract the first I to-be-deployed collaborative combinations in the order of D1, D2, ..., Da, and transmit these I to-be-deployed collaborative combinations and their center point coordinates to the mobile devices of the deep foundation pit monitoring personnel. If I1 < I, transmit all the to-be-deployed collaborative combinations and their center point coordinates to the mobile devices of the deep foundation pit monitoring personnel, where I is the preset threshold for the number of small processor devices to be deployed.

8. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 7 is characterized in that: Based on the center point coordinates of each to-be-deployed collaborative combination, the deep foundation pit monitoring personnel deploy a small processor device at the corresponding position in the deep foundation pit to receive and analyze the monitoring values of several parameters of the corresponding to-be-deployed collaborative combination.

9. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 8 is characterized in that: For each successfully deployed small processor device by the deep foundation pit monitoring personnel, input the IP address of this small processor device into the sensors at the monitoring points of several parameters included in the corresponding to-be-deployed collaborative combination, and set the priority transmission strategy to preferentially transmit the monitoring values of the corresponding parameters collected to this small processor device.

10. The deep foundation pit automatic monitoring system based on the Internet of Things according to claim 9 is characterized in that: After all the small processor devices corresponding to the to-be-deployed collaborative combinations are deployed, the on-ground analysis and early warning module first extracts from the deep foundation pit early warning model the collaborative early warning sub-models for analyzing several parameters included in each to-be-deployed collaborative combination, and then transmits the corresponding collaborative early warning sub-models to the corresponding small processor devices according to the IP addresses of the small processor devices deployed corresponding to each to-be-deployed collaborative combination.

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