Urban Underground Space Resistivity Sensing System and Data Acquisition Method Based on Cloud-Edge-Device Collaboration

Abstract

The present invention discloses an urban underground space resistivity perception system and a data acquisition method based on cloud-edge-terminal collaboration. Based on the advanced cloud-edge-terminal architecture design, the data acquisition work is sunk to distributed edge nodes and sensing nodes for execution, and the data processing and data mining work with intensive computing tasks are deployed on the central cloud computing platform, meeting the real-time and efficient requirements of data acquisition. At the same time, a three-dimensional space random distributed sensing network is constructed by combining well and ground. Taking full advantage of the favorable conditions of having buried horizontal cables and arranged longitudinal boreholes on both sides of the road, a three-dimensional resistivity perception network with cross-street penetration is flexibly arranged to make up for the deficiencies of single surface exploration and achieve fine imaging of the targets under urban streets.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical prospecting, and particularly relates to an urban underground space resistivity perception system and a data acquisition method based on cloud-edge-terminal collaboration. Background Art

[0002] Urban underground engineering is the foundation and an important part of urban construction, and it itself has the characteristics of being hidden and invisible. With the acceleration of the urbanization process, the health status and safety of the urban underground space structure are directly related to the life and property safety of urban residents. However, how to quickly, effectively, and non-destructively evaluate and assess the health status of the underground space structure is still a difficult task facing urban management departments. At present, most government departments strengthen supervision in the design, construction and other links to ensure that the quality of underground engineering construction meets the standards. However, the service life and safety of underground engineering are not only closely related to the structural design and construction quality, but also closely related to the environmental changes around the underground engineering in the later stage. The influence of the underground structure and the surrounding environment is mutual and progressive. The formation deformation and stress change around the underground structure may affect the underground structure or even cause structural damage; while the damage of the underground structure will promote the rheology of the surrounding soil medium and the migration of groundwater, accelerating the damage of the underground structure. Therefore, it is necessary to comprehensively consider and systematically study the underground space structure, the surrounding geological environment, the underground pipeline network structure, the human and traffic environment, etc. as an organic whole from the dimensions of time and space. Among them, using the drilling, geophysical exploration and other data accumulated in the past historical period to build a "transparent city" is a key step, and it is also an important basic support for the construction of a "smart city". And using emerging scientific and technological means to build a four-dimensional dynamic perception network for cities is a necessary way for the construction of a "smart city", which is of great significance for urban modernization and the construction of a livable environment.

[0003] The changes in the underground structure and its surrounding geological environment will correspondingly cause changes in the physical property parameters of the underground medium (such as density, elastic wave velocity, resistivity, etc.). The dynamic monitoring of these physical property parameter changes by the underground perception system is equivalent to installing dynamic "physical examination" sensors on the "body" of the city, to real-time perspective, perceive, and monitor the dynamic changes of the urban underground pipeline network and the underground space structure. When the set critical value is reached, an abnormal warning message is triggered in time, and the location where the abnormality occurs is quickly located through a distributed multi-sensor monitoring network, which is convenient for timely disposal and protection of life and property safety. However, at present, the urban underground perception system mainly uses contact sensors such as temperature, underground water level, stress, and displacement for in-situ measurement, lacking sensors with penetration imaging capabilities for long-term, remote, and non-contact intelligent perception.

[0004] High-density resistivity method is a multi-channel, array exploration technology developed on the basis of ordinary electrical exploration. High-density electrical method only needs to lay cables and electrodes once to obtain a large amount of detection data, which not only saves manpower and material resources, but also improves data acquisition efficiency, and the imaging results are intuitive and easy to interpret. However, there are still some difficulties and obstacles in the long-term monitoring (intelligent perception) of urban underground targets (such as road cavity collapse) using traditional high-density resistivity method:

[0005] 1. The environment around urban streets is complex: ①Under the urban streets are areas where various pipe networks such as urban electricity, water supply, and communications pass through, and the underground environment is extremely complex. ②The grounding conditions on both sides of the urban streets are very different, and electromagnetic interference is serious. ③The electrodes can only be laid out along both sides of the narrow and long urban streets. There is a lack of longitudinal expansion space required for the arrangement of three-dimensional high-density resistivity measurement points, and it is difficult to arrange regular surface measurement networks. ④Although borehole resistivity imaging has a higher resolution, it is only a vertical detection and the cross-hole measurement spacing is limited. At present, the respective advantages of surface and borehole resistivity imaging have not been fully utilized. Joint well-ground exploration is more conducive to fully utilizing the advantages of resistivity imaging.

[0006] 2. The existing resistivity sensing system design is highly dependent on municipal facilities and is also constrained by the actual location of municipal facilities, making it difficult to effectively exert its flexibility; the existing design is also constrained by the small number of electrode channels in the sensing node, resulting in a small number of power supply and potential measurement combination types, which seriously affects the sensing imaging effect. In addition, since the power supply and potential measurement electrode combinations may belong to different sensing nodes, the time delay of the existing serial wired transmission network will have an important impact on the synchronization of power supply and potential measurement, and the response delay superimposed on the response waiting will also seriously affect the data acquisition efficiency.

[0007] 3. The resistivity sensing system that uses a central console for remote centralized management has inherent defects: ① All sensing nodes are centrally managed and scheduled through a single central console, which may cause poor or congested instruction and data transmission due to network busyness, resulting in time delays, bit errors and string loss, and real-time and reliability cannot be guaranteed. ② Massive data is collected and centrally stored and processed in the central console, which has high requirements for the software and hardware of the central console; it fails to make full use of general public computing resources, resulting in duplicate construction and waste of resources, and also increases the subsequent operation and maintenance costs of the system itself.

[0008] 4. The main feature of the intelligent sensing system is highly automated remote intelligent telemetry: unattended automatic collection, remote data automatic transmission and automatic storage, automatic data processing, automatic analysis prediction and alarm. However, the existing resistivity sensing system still has a big gap, focusing only on data collection, but lacking the ability to extract and mine data information, far from reaching the level of intelligent perception, and lacking artificial intelligence prediction and analysis capabilities.

[0009] Therefore, it is necessary to design a brand-new resistivity sensing system, which relies less on municipal facilities, can make full use of general-purpose wireless Internet of Things systems, edge clouds (edge storage and edge computing), and central clouds and other general public resource platforms, and relies on cutting-edge technologies such as big data and artificial intelligence to achieve an intelligent sensing system with the ability to predict and evaluate intelligent risks. Summary of the Invention

[0010] In view of the deficiencies of the prior art, the present invention provides a resistivity sensing system and data acquisition method for urban underground spaces based on cloud-edge-end collaboration. The specific technical solutions are as follows:

[0011] A resistivity sensing system for urban underground spaces based on cloud-edge-end collaboration. The system adopts a cloud-edge-end architecture design, including a central cloud computing platform, multiple edge servers distributedly network-connected to the central cloud computing platform, and multiple resistivity sensing nodes distributedly network-connected to each edge server;

[0012] The central cloud computing platform is used to manage the entire resistivity sensing system, including: setting up and configuring distributed edge servers, and managing all resistivity sensing nodes through the edge servers; performing global data processing and model inversion, including comparing and mining real-time data and historical data, and sending the model results to the edge servers to guide preliminary data analysis; alarming and reporting data anomalies exceeding the threshold;

[0013] The edge server is an edge node, which is used to control multiple resistivity sensing nodes within the sharded control domain to work collaboratively, including: collaborating and controlling the selection and acquisition process of power supply and potential measurement electrode pairs within the domain; screening, sorting, and storing the data obtained within the domain according to the designed format, and uploading the data to the central cloud computing platform for backup at the same time; after data acquisition is completed, comparing and analyzing the real-time data with the historical data and the model calculation results of this area fed back by the central cloud computing platform to the edge node according to the historical data to determine whether there are any anomalies; when there are abnormal changes, reporting the abnormal information to the central cloud computing platform;

[0014] The resistivity sensing node is an end node, and multiple resistivity sensing nodes are arranged horizontally along urban roads and / or in vertical wells; each resistivity sensing node is an independent resistivity sensor unit, which includes a collection station, a multi-channel electrode conversion switch connected to the collection station, a multi-core high-density electrical method cable, and a grounding electrode connected to the multi-core high-density electrical method cable; the resistivity sensing node respectively executes power supply or potential measurement tasks according to the instructions of its affiliated edge node, and uploads the measurement data to the corresponding edge node.

[0015] Further, when the resistivity sensing nodes are arranged horizontally along urban roads, the cables in the resistivity sensing nodes are multi-core segmented cascaded high-density electrical method cables. The segmented cascaded cables are connected in series into a whole cable through a cascaded electrode conversion switch, and the acquisition station is connected to the end of the whole cable;

[0016] When the resistivity sensing nodes are arranged vertically along vertical wellbores, the cables in the resistivity sensing nodes are single-strip centralized high-density electrical method in-well cables. Multiple electrode junctions are arranged at equal intervals on the cable, and each electrode junction serves as a grounding electrode. The top of the cable is connected to the acquisition station through a centralized electrode switch;

[0017] When the resistivity sensing nodes are arranged horizontally along urban roads and in combination with wellbores, the single-strip centralized high-density electrical method cable arranged in the wellbore is first connected to one end of the multi-core segmented cascaded high-density electrical method cable on the ground through a centralized electrode conversion switch, and the acquisition station is connected to the other end of the segmented cascaded high-density electrical method cable; and multiple electrode junctions are arranged at equal intervals on the centralized high-density electrical method cable, and each electrode junction serves as a grounding electrode.

[0018] Further, the acquisition station includes a control module, a power supply module, a potential measurement module, a communication module, and a GPS module;

[0019] Under the command of the affiliated edge node, the control module controls the other several modules of this acquisition station to realize the operation management, self-check, communication with the edge node, and function interchange of power supply / potential measurement, channel selection, acquisition process execution, and data storage and upload of measurement data under the control of the acquisition instruction of the acquisition station itself;

[0020] After receiving the power supply instruction, the power supply module selects the corresponding electrode channel through the control module and supplies power to the ground through the cable channel and electrode connected thereto, and simultaneously measures the magnitude of the supply current. After the power supply is completed, it uploads the node number, its power supply channel number, the measurement start time, and the supply current value;

[0021] After receiving the potential measurement instruction, the potential measurement module selects the corresponding electrode channel through the control module and measures the potential through the cable channel and electrode connected thereto, and simultaneously measures the magnitude of the potential difference; after the measurement is completed, it uploads the node number, its potential measurement channel number, the measurement start time, and the potential difference value;

[0022] The GPS module is used for precise timing and coordination of each node.

[0023] Further, the edge node and the end node perform remote data transmission through a mobile communication network, and the edge node and the central cloud computing platform perform remote data transmission through a wired network.

[0024] A method for collecting resistivity data of urban underground space based on cloud-edge-terminal collaboration, which is implemented based on the above-mentioned system. The method specifically includes the following steps:

[0025] (1) Determine the layout method and acquisition parameters of resistivity sensing nodes according to the actual situation of the target street, the maximum exploration depth, and the resolution of underground detection targets;

[0026] (2) Arrange resistivity sensing nodes on the target street. The central cloud computing platform assigns a unique system number to each edge node, the edge node assigns a unique system number to each resistivity sensing node within its domain, and the sensing node assigns a unique system number to each electrode point within its system. At the same time, collect the three-dimensional geographical coordinates of each electrode point;

[0027] (3) The central cloud computing platform sequentially selects different edge nodes for block measurement. The selected edge node selects a sensing node as the power supply node in the order of the resistivity sensing node numbers, and then selects an electrode combination within the sensing node as the power supply electrode pair AB. The electrode combination within the edge node domain to which the sensing node belongs is used as the potential measurement electrode pair MN, and the potential measurement electrode pair MN belongs to the same sensing node. Judge whether the distance between the measurement electrode pair MN and AB is within the effective measurement radius r of AB. If so, perform power supply and potential measurement; if not, move to the position of the next ABMN combination for a new measurement condition judgment; the effective measurement radius r of AB ≤ n·a, where n is the effective radius coefficient, n = 6 - 14, and a is the AB spacing; until all the power supply electrode pairs within the sensing node and the combinations of multiple paired potential measurement electrode pairs are traversed, the power supply and potential measurement process when the sensing node is used as the power supply node is completed;

[0028] (4) Sequentially move to the next resistivity sensing node to perform the power supply and potential measurement process until all the power supply electrode combinations of the last sensing node are completed, then the entire power supply and potential measurement process of the current edge node is completed;

[0029] (5) Then enter the next edge node to perform the same power supply and potential measurement process until all edge nodes are traversed;

[0030] (6) After the acquisition work is completed, the edge node notifies each sensing node to upload the collected data and its own status information. The edge node formats the data in this area, quickly compares it with the model results of this area downloaded from the central cloud computing platform, and gives the processing and analysis results; the edge node reports the preliminary processing and analysis results to the central cloud computing platform, and the central cloud computing platform distributes the feedback of the model results of intelligent analysis based on historical data and other multi-source data to each edge node to guide subsequent edge nodes to perform rapid anomaly analysis and risk identification.

[0031] Further, when AB is used as the power supply electrode pair, the potential measurement electrode pairs MN that meet the conditions between different sensing nodes are synchronously measured under the time synchronization of the GPS module. That is, multiple potential measurement electrode pairs MN between different nodes work in parallel with one power supply electrode pair AB at the same time, realizing one power supply and multiple measurements.

[0032] Further, when selecting the power supply electrode pair, it is carried out in the principle of increasing the serial numbers of the electrodes from small to large. Taking the end where the acquisition station is located as the starting point, taking the electrode point closest to the acquisition station as electrode A, and selecting the electrode point with the serial number interval between AB equal to 1 as electrode B to implement power supply; then keeping the serial number interval of AB, shifting A and B to the next electrode point in sequence until point B reaches the last electrode point of the current sensing node, then the power supply process with the serial number interval between AB equal to 1 is completed;

[0033] Then starting from the starting point, select the measurement points with a serial number interval of 2 between AB to implement power supply, and then shift A and B in sequence until point B reaches the last electrode point, then the power supply process with a serial number interval of 2 between AB is completed;

[0034] Repeat changing the AB interval until the set maximum isolation coefficient is reached, then the power supply process of this sensing node is completed.

[0035] Further, when the resistivity sensing nodes are arranged at one time, and the positions of each electrode point are fixed and have accurate position coordinates, the edge node corresponding to this resistivity sensing node calculates and makes the power supply and potential measurement acquisition table in advance. In this table, the sensing node numbers, electrode numbers of each power supply point AB, and the sensing node numbers, electrode numbers of the corresponding multiple potential measurement points MN are arranged in sequence, so that the actual acquisition is carried out according to the sequence of this table, and the entire data acquisition process is completed.

[0036] Compared with the prior art, the present invention has the beneficial effects that:

[0037] 1. The "cloud-edge-terminal" architecture is adopted to realize hierarchical storage and hierarchical processing of sensing data. By using the edge server that sinks to the network edge, it realizes data acquisition and data storage close to the acquisition end, in a sub-region, and in a distributed manner, avoiding affecting the acquisition process due to network congestion, and improving the real-time performance and response efficiency of data acquisition. And let the "central cloud" give play to its computing power advantages, responsible for data processing, data mining and risk prediction of large amounts of data. Adopting the cutting-edge architecture design of distributed and centralized division of labor and cooperation of the "cloud-edge-terminal" greatly improves the ability and efficiency of the resistivity sensing system.

[0038] 2. Design multi-channel, randomly distributed resistivity sensing nodes with infinite load-carrying capacity, which not only ensures the efficiency of the connection between electrode channels and the high-density power supply / potential measurement combination type, but also minimizes the number of sensing nodes and remote transmission devices, reducing the system construction cost.

[0039] 3. Adopt a combined surface and borehole method to construct a three-dimensional space randomly distributed sensing network. Make full use of the favorable conditions on both sides of the road for burying horizontal cables and arranging vertical boreholes, and flexibly arrange a three-dimensional resistivity sensing network with cross-street penetration to make up for the deficiencies of single surface exploration and achieve fine imaging of targets below the street.

[0040] 4. Achieve remote intelligent sensing through the combination of wireless mobile communication networks and wired public networks. Make full use of the zoning characteristics of mobile communication networks in the city to automatically realize the zoning and hierarchical management of the resistivity sensing network. The large-capacity load-carrying capacity of mobile communication networks enables the number of sensing nodes to be unrestricted, and the scale of the sensing system can be flexibly adjusted. The ability of mobile communication networks to automatically connect to the urban high-speed backbone network simplifies and improves the feasibility of the cloud-edge-end design.

[0041] 5. Through the combination of the cloud computing platform and artificial intelligence, realize the automated, intelligent processing and mining of resistivity sensing data, and predict and alarm. Make full use of existing artificial intelligence of things (AIoT) technologies as the carrier for remote information transmission and data mining of the resistivity sensing network to realize the intelligent analysis and prediction and alarm of resistivity sensing information.

[0042] 6. Make full use of public communication networks and public computing resource platforms, which not only avoid system duplication and resource waste, but also save the later maintenance cost. Let the system construction focus on the construction of front-end sensing nodes, data acquisition methods, and system architecture design. Moreover, the performance of the system relying on the public Internet of Things construction will be automatically upgraded as a whole with the update of the public Internet of Things system. Only the sensing node unit needs to be maintained and upgraded, and the system investment is relatively small while the system expansion ability and adaptability are significantly enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the resistivity sensing system architecture used in the present invention;

[0044] Figure 2 Schematic diagram of the structure of the acquisition station and the connection method of the cables used in the present invention;

[0045] Figure 3 Schematic diagram of the layout method of the resistivity sensing system used in the present invention;

[0046] Figure 4 Schematic diagram of the layout of the ground and borehole electrodes on both sides of the road of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048] I. System Structure

[0049] The resistivity sensing system for well - ground combination is designed based on the "cloud - edge - terminal" architecture, and is composed of sensing nodes (terminal), edge cloud that sinks close to the sensing nodes and is responsible for coordinating the data acquisition process (edge), a central cloud computing platform dedicated to centralized data processing and data analysis (cloud), and wireless and wired transmission networks for connecting the cloud, edge, and terminal. The system can be divided into three components: the sensing layer, the edge computing layer, and the central cloud computing layer ( Figure 1 ).

[0050] a. Sensing layer

[0051] The sensing layer is composed of numerous resistivity sensing nodes, which are horizontally arranged on both sides of urban roads or arranged in combination with vertical well holes, forming cross - street penetration and well - ground resistivity joint imaging to achieve four - dimensional three - dimensional sensing of the resistivity of the area below the street.

[0052] The resistivity sensing node is an independent resistivity sensor unit, which includes an acquisition station (as shown in Figure 2 a), a multi - channel electrode conversion switch connected to the acquisition station, a multi - core high - density electrical method cable, and grounding electrodes connected to the multi - core high - density electrical method cable. The high - density electrical method cable specifically refers to a multi - core cable with equally spaced taps and conductive joints.

[0053] There are the following three layout methods for the resistivity sensing nodes:

[0054] (1) Horizontally arranged along urban roads

[0055] At this time, the cable in the resistivity sensing node is a multi - core segmented cascaded high - density electrical method cable, and the segmented cascaded high - density electrical method cable is connected in series into a whole cable through a cascaded electrode conversion switch. The acquisition station is connected to the end of the whole cable. The segmented cascaded high - density cable carries 8 - 10 electrodes. The connection method between the cable and the acquisition station is as shown in Figure 2 b.

[0056] (2) Arranged along vertical well holes

[0057] At this time, the cable in the resistivity sensing node is a single cable, and the cable adopts an integrally formed centralized structure design to ensure watertightness. Multiple electrode knots are equally spaced on the cable, and each electrode knot serves as a grounding electrode. The top of the cable is connected to the acquisition station through a centralized electrode switch. The connection method between the cable and the acquisition station is as shown in Figure 2 c.

[0058] (3) Horizontally arranged along urban roads and arranged in combination with vertical well holes

[0059] At this time, the cable arranged in the borehole is still a single centralized high-density electrical method borehole cable, and multiple electrode junctions are equidistantly arranged on this cable, with each electrode junction serving as a grounding electrode. The horizontally arranged cable is a multi-core segmented cascaded high-density electrical method cable. However, at this time, the single centralized high-density cable arranged in the borehole needs to be first connected to one end of the multi-core segmented cascaded high-density cable on the ground through a centralized electrode conversion switch, and then the acquisition station is connected to the other end of the segmented cascaded cable to form a resistivity sensing node. The connection method between the cable and the acquisition station is as Figure 2 shown in d.

[0060] Among them, the multi-core segmented cascaded high-density electrical method cable carries 8 - 10 electrodes, and the centralized high-density electrical method borehole cable is a watertight integrally formed cable containing 30 - 60 electrode junctions.

[0061] The acquisition station consists of a control module, a power supply module, a potential measurement module, a communication module, and a GPS module. The acquisition station executes power supply or potential measurement tasks respectively according to the instruction requirements of the affiliated edge node. The communication module relies on mobile communication technology to be responsible for the communication between the acquisition station and the affiliated edge node. The control module is commanded by the edge node instruction, and controls the other several modules of this acquisition station to realize the operation management, self-check, communication with the edge node of the acquisition station itself system, and the control of each module of the system in a series of processes such as the measurement role (power supply / potential measurement) interchange, channel selection, acquisition process execution, and data saving and uploading measurement data under the control of the acquisition instruction.

[0062] After receiving the power supply instruction sent by the edge node, the power supply module selects the corresponding electrode channel through the control module and supplies power to the ground through the connected high-density electrical method cable and electrode, and simultaneously measures the magnitude of the supply current. After the power supply is completed, it uploads the node number, its power supply channel number, the measurement start time, and the supply current value.

[0063] After receiving the potential measurement instruction sent by the edge node, the potential measurement module selects the corresponding electrode channel through the control module and measures the potential through the connected cable and electrode, and simultaneously measures the magnitude of the potential difference. After the measurement is completed, it uploads the node number, its potential channel number, the measurement start time, and the potential difference value. When the power supply and potential measurement belong to different sensing nodes, the edge node coordinates the measurement start times of different nodes (synchronized by GPS time). When the power supply and potential measurement belong to the same node, the program of the acquisition station itself coordinates the start times of the power supply and potential measurement.

[0064] The communication module uses a mobile communication module of 5G or above, supporting MEC edge access and edge computing modes. It directly controls the acquisition process through the edge server and coordinates the selection of power supply / potential measurement channels among various sensing nodes. After the measurement is completed, the acquired result data is directly uploaded through the mobile communication network and stored in the edge server.

[0065] The GPS module is used for precise time synchronization of each node. The power supply / potential measurement process involves cooperation among different nodes, and using the precise time synchronization of GPS satellites is an efficient and simple way to keep different nodes in step. The GPS module includes a GPS antenna and its interface connecting wire.

[0066] b. Edge computing layer

[0067] The resistivity sensing nodes, as independent data acquisition units, are equal in status and operate independently of each other. However, they need to cooperate with each other to complete the combined measurement (power supply / potential measurement) between different nodes and achieve cross-street through imaging. The cooperation between different sensing nodes requires a higher-level control unit to plan and coordinate. The traditional solution is to design a central console to remotely control all sensing nodes through the network. When there are many sensing nodes, there are problems such as network congestion and latency, and there are many problems with the real-time performance, reliability, and acquisition efficiency of this remote centralized control method. The centralized control method of the central console seems powerless and difficult to continue. Therefore, it is necessary to "sink" and move forward the acquisition control to multiple mobile edge servers close to the acquisition nodes for implementation, forming distributed edge control nodes to achieve near deployment and near control.

[0068] The edge nodes are set and configured by the central cloud computing platform, forming multiple edge servers distributed throughout the sensing network. Each edge server shards and controls the cooperation of multiple sensing nodes within its domain. The main tasks of the edge nodes mainly include: ① Data acquisition, coordinating and controlling the selection of power supply and potential measurement electrode pairs within the domain and the acquisition process control. ② Data storage and data transmission, screening and sorting the data within the domain obtained from data acquisition and storing it in the edge cloud in the designed format, and at the same time uploading the data to the central cloud for backup for subsequent centralized processing of the whole-domain data. ③ Preliminary data processing. After the data acquisition is completed, compare and analyze the real-time data with the historical data and the model obtained from the historical data (the calculation result of the regional model fed back from the central cloud to the edge node). Compare the differences. If there are abnormal changes, report the abnormal information to facilitate further comprehensive analysis and processing by the central cloud computing platform.

[0069] The edge cloud layer is also the network transmission layer where distributed wireless networks converge and concentrate on wired public networks. It uses a combination of wireless mobile communication networks and wired Internet to achieve remote data transmission and collection instruction transmission. The mobile communication network requires the use of 5G and above communication platforms, and uses edge servers to achieve collection control nearby, realizing distributed, near-end efficient control of collection nodes and distributed storage of collected data.

[0070] Mobile communication networks have unparalleled mobility and flexibility over wired networks, and are particularly suitable for dynamically increasing or decreasing sensing nodes and adjusting the location of sensing nodes. Moreover, data transmission is transmitted through the nearest base station (distributed), avoiding the congestion of the wired transmission channel. The regional and distributed network structure of mobile cellular base stations is highly consistent with the regional and distributed layout of sensing nodes, which is conducive to the smooth transmission of instructions and data. The advantage of using mobile communication networks for remote data transmission is that it can make full use of the existing public communication network, avoiding repeated investment in the construction of wired sensing networks and greatly saving costs and financial investment; it can also make full use of efficient and stable public network resources to achieve seamless connection between wireless and wired networks, and avoid the later system operation and maintenance costs of the self-built network transmission layer.

[0071] c. Central cloud computing layer

[0072] The central cloud computing is realized by relying on the resources of the general public cloud computing platform. Compared with the edge cloud, the large-scale parallel computing capability of the central cloud is particularly suitable for the needs of high-performance computing such as massive sensing data processing, and is used for the storage and intelligent processing and analysis of the entire resistivity sensing data.

[0073] The main tasks of the central cloud include: ① Operation and management of the entire sensing network, including setting up and configuring distributed edge servers, and then managing all resistivity sensing nodes through edge servers. ② Global data processing and model inversion, including comparison and mining of real-time data and historical data, and sending model results to edge servers to guide preliminary data analysis. ③ Abnormal data alarms that exceed the threshold are reported to the city brain for comprehensive analysis and disposal of multi-source data.

[0074] Therefore, the construction of a complete and feasible resistivity sensing system is completed through the coordination and cooperation of the central cloud, edge cloud and sensing nodes. The coordination between the central cloud and the edge cloud is constrained and task allocated through the federated computing paradigm. Under the framework of the federated computing paradigm, the central cloud and the edge cloud, as well as the edge clouds, dynamically configure task objectives through cloud-edge coordination and game, achieve coordination and division of labor, and jointly maintain and ensure the normal operation of the entire system and the forward and reverse transmission of data flows (information feedback).

[0075] The Urban Brain is also built based on the central cloud. Relatively speaking, it receives the convergence of multi-source data and has higher data integration and intelligent decision-making support capabilities. It is the ultimate export of the perception results of the present invention.

[0076] II. Data Acquisition Method

[0077] 1. Arrangement of Resistivity Sensing Nodes

[0078] The arrangement of resistivity sensing nodes mainly focuses on the electrode arrangement and is divided into two methods: horizontal arrangement on the ground surface and vertical wellbore arrangement. Multiple factors need to be comprehensively considered, such as electrode spacing, total number of electrode channels, cable laying method, location of the acquisition station (involving external power supply), and the arrangement of mobile communication antennas and GPS antennas. The segmented cable for horizontal laying is shallowly buried along the green belts or sidewalks on both sides of the street. The cable in the well is drilled and laid at appropriate positions at the street intersections or on the roadside. It is recommended to arrange the wellbores symmetrically on both sides of the road (pay attention to avoiding underground buried pipelines and cables). The length of the cable in the well is recommended to be arranged from 30m to 60m, and the electrode spacing is 0.5 to 1m. The positions of the horizontal electrode points meet the principle of random distributed electrode arrangement, that is, there are no special requirements for electrode spacing and position, and they should be arranged as evenly as possible when conditions permit. After the electrode points are arranged, surveying and mapping equipment such as GPS and total station are used to collect the three-dimensional geographical coordinates of each electrode point in a timely manner and input them into the system for subsequent data acquisition and data processing.

[0079] Because the power supply of the acquisition station needs to use the city power for boosting, it is necessary to plan and design the location of the acquisition station according to the conditions on both sides of the street and build a fixed equipment box for the access of city power, boosting power supply, and the placement of the acquisition station. Multiple nearby acquisition stations can consider sharing the same equipment box ( Figure 3 such as B03, S03, and S04, as well as B04, S06, S07, and S08 in

[0080] The horizontal cable can be arranged in a single-line, U-shaped double-line, or S-shaped multi-line shape. Figure 3 such as S02, S03, S06, etc. in Figure 3 are single lines, S01 and S09 are U-shaped double lines, where S01 is a U-shaped arrangement connected to both sides of the street through a cross-street cable, and S14 is a U-shaped double line arranged on one side of the street. When there is a certain open area on one side of the street, S-shaped multi-lines can be arranged ( Figure 3In the case of S07 and B06, they can be measured separately using independent acquisition stations, or the cable in Well B06 can be connected to the end of S07 to save the acquisition station for B06. The advantage of the well-ground series connection is that the well-ground electrode measurement is directly completed inside the S07 acquisition station with seamless docking, and there are more combined measurement methods. The disadvantage is that with more measurement points, the acquisition time becomes relatively longer.

[0081] 2. Acquisition parameter settings

[0082] The present invention uses the random dipole device as the unified device type. This device type includes both regular device types such as Wenner, Schlumberger, dipole-dipole, etc., and various asymmetric and non-collinear device types. Therefore, the random dipole device is the normalized expression form of all device types, and the dipole moment and electrode spacing parameters are dynamically adjustable, having wide adaptability and flexibility, which is conducive to flexibly realizing the observation settings for complex and special requirements (crossing road branches and non-equidistant electrode settings), and the dipole-dipole device has a relatively high detection resolution. In addition to the device type setting, the acquisition parameter setting has a decisive influence on the resolution and exploration depth of the actual measurement. Therefore, it is also necessary to design appropriate acquisition parameters to obtain the best detection effect.

[0083] (1) Optimal electrode spacing

[0084] The electrode spacing refers to the distance between the electrodes placed front and back in the electrode array. In the random distribution system, the actual electrode positions can float and change according to the surface conditions. Since the electrode spacing determines the detection depth, imaging resolution, and system construction cost, although the electrode spacing can float and change, considering the balance point between the exploration depth and resolution, there is still an optimal electrode spacing value range. When actually arranging the electrode points, it is recommended to arrange the electrodes with reference to the optimal electrode spacing.

[0085] (2) Maximum isolation factor

[0086] Assume that the electrode spacing is p, then the spacing between the power supply and potential measurement points can be p, 2p, 3p, 4p up to the maximum interval N*p (N is the number of electrode channels of the instrument system) of the array. However, in actual measurement, a maximum isolation factor (m <= N) is often estimated and set according to the maximum exploration depth h: _

[0087] m = h / (λ×p) (1)

[0088] where λ = 2 - 3. During data acquisition, the spacing between the power supply electrode pair AB, the potential measurement electrode pair MN (dipole moment), and the spacing between the AB and MN electrode pairs (electrode spacing) increase in sequence from isolation factor 1 to m, traversing all possible position combination types of ABMN.

[0089] (3) Effective measurement radius:

[0090] Because it involves well - ground joint detection and monitoring, it is necessary to consider the distribution of power supply and potential measurement points in three - dimensional space and the problem of effective measurement radius. Due to the inhomogeneity of underground media, there are differences in the effective measurement range when the measurement points are located in different media. Therefore, the effective measurement range of well - ground joint measurement is not a perfect and symmetric spherical space. However, considering that the effective measurement range itself is also affected by various factors such as the measurement accuracy of the instrument and the magnitude of the supply current, and has a certain elastic change space, it can still be simplified into an "effective measurement spherical domain" to screen measurement points and improve the measurement efficiency and effect. The radius of the "effective measurement spherical domain" is the effective measurement radius R. Outside the effective measurement radius R, the potential difference of the dipole - dipole device decreases rapidly with the increase of the electrode spacing and quickly drops below the effective measurement accuracy of the instrument. R <= n * a (where n is the effective radius coefficient and a is the dipole moment of the power supply electrode pair), and generally n = 6 - 8. The present invention adopts a dynamic variable dipole moment measurement design, which can effectively improve the reading accuracy of the instrument. Therefore, it is recommended to select the actual perception radius coefficient n between 6 and 14. The purpose of setting the effective radius is to set the measurement threshold according to the effective measurement radius during actual data acquisition, exclude most of the measurement processes beyond the effective measurement radius, and improve the data acquisition efficiency.

[0091] During the measurement process, the distances between the power supply electrode pair AB and the measurement electrode pair MN should be calculated in real - time according to the following formula:

[0092] Let the coordinates of points A and B be (x A , y A , z A ) and (x B , y B , z B ) respectively. Then the coordinates of the mid - point O of AB are:

[0093] x O =(x A +x B ) / 2 y O =(y A +y B ) / 2 z O =(z A +z B ) / 2 (2)

[0094] Let the coordinates of points M and N be (x M , y M ) and (x N , y N ) respectively. Then the coordinates of the mid - point O1 of MN are:

[0095] x O1 =(x M +x N ) / 2 y O1 =(yM +y N ) / 2 z O1 =(z M +z N ) / 2 (3)

[0096] Then the distance L between OO’ is

[0097]

[0098] Then, compare with the set effective measurement radius. The MN points that exceed will be cancelled from measurement, accelerating the data acquisition process.

[0099] 3. Selection of power supply electrode pair and measurement electrode pair

[0100] During the data acquisition process of the present invention, because the system supports uneven and random distribution of measurement points, the distances between AB and MN will increase as the isolation factor (multiple of electrode distance) increases. It is necessary to obtain the position information of all measurement points in advance through positioning measurement before acquisition, calculate the effective measurement radius that dynamically changes with the measurement process in real time according to the positions among ABMN, and control the acquisition point selection process.

[0101] The entire measurement process of the present invention is carried out around the power supply process, that is, traversing all possible power supply electrode combinations (power supply electrode pair AB) within each sensing node. For each power supply combination, then traverse to find all possible potential measurement electrode combinations (potential measurement electrode pair MN within the effective measurement sphere area in the power supply node or surrounding nodes) corresponding to the power supply electrode pair AB, realizing "one power supply with multiple measurements". The measurement process traverses all power supply points in the order of edge node number and sensing node number until the measurement of the last power supply point is completed, then the single data acquisition process of the entire measurement area is completed. Then repeat the measurement process at the set time interval to achieve four-dimensional dynamic perception. When an anomaly is found in a certain area, the observation frequency can be adjusted to perform encrypted measurement of the whole area or the anomaly section (set at the edge node), and at the same time cooperate with other detection means and on-site inspections to verify the anomaly.

[0102] The specific implementation process is as follows:[[]]

[0103] (1) The central cloud computing platform sequentially selects different edge nodes for block measurement in order (the selected edge nodes are active nodes, and other unactivated edge nodes are in a dormant state). The selected edge node selects a sensing node as the power supply node in the order of the sensing node numbers, and then selects an electrode combination within the sensing node as the power supply electrode pair AB. The electrode combination within the edge node domain to which the sensing node belongs is used as the measurement electrode pair MN, and the measurement electrode pair MN belongs to the same sensing node. It is judged whether the distance between the measurement electrode pair MN and AB is within the effective measurement radius r of AB. If so, power supply and potential measurement are performed; if not, move to the position of the next ABMN combination for a new measurement condition judgment; until all combinations of the power supply electrode pairs and potential measurement electrode pairs within the sensing node are traversed, the power supply and potential measurement process when the sensing node is used as the power supply node is completed;

[0104] (2) Sequentially move to the next resistivity sensing node to perform the power supply and potential measurement process until all combined electrode pairs of the last sensing node are powered, and the entire power supply and measurement process of the current edge node is completed;

[0105] (3) Then enter the next edge node to perform the same power supply and measurement process until all edge nodes are traversed.

[0106] In addition, in order to ensure the completeness of the collected data and improve the clarity of the underground space imaging, when selecting the power supply electrode pair, the electrode distance of AB is continuously changed in the following manner:

[0107] ① The edge node sequentially selects the sensing node as the power supply node, then the power supply electrode pair AB is only traversed and selected within the sensing node. The selection of points A and B is carried out in the order of the electrode point numbers from small to large. First, start from the end close to the acquisition station (starting point), select the electrode point closest to the acquisition station as electrode A, and select the electrode point with the AB serial number interval equal to 1 as electrode B for power supply; then keep the AB serial number interval, shift A and B to the next electrode point in sequence until point B reaches the last electrode point of the current sensing node, and the power supply process with all AB serial number intervals equal to 1 is completed;

[0108] ② Then start from the starting point, select the measurement points with an AB interval of 2 serial numbers for power supply, and then shift A and B in sequence until point B reaches the last electrode point, and the power supply process with an AB interval of 2 serial numbers is completed;

[0109] ③ Repeat changing the AB serial number interval until the set maximum isolation coefficient is reached, and the power supply process of the sensing node is completed. Then select the next sensing node, repeat the above power supply point selection process and perform power supply.

[0110] Each power supply has the edge node set the power supply start time, power supply parameters, etc. After the power supply is completed, the power supply electrode number, start time, and power supply current value are saved. After the entire measurement is completed, the entire data is uploaded to the edge node for processing and sorting.

[0111] At the same time, in order to improve the data acquisition rate, the present invention processes the measurement electrode pairs in two acquisition methods: within the node and between nodes:

[0112] ① Sequential serial measurement is adopted within the node: within a node, each time power is supplied, according to the MN sequence table, one of the measurement electrode pairs MN is selected for potential measurement. Then other paired MNs are selected for the next power supply and potential difference measurement. Each time a measurement is made, one of the multiple combinations of MN within a node needs to be selected in sequence to execute the measurement process.

[0113] ② Simultaneous parallel measurement is adopted between nodes: when MN is located in other nodes not where AB is, each time a measurement is made when AB is powered, the MNs located in different nodes perform potential measurement simultaneously. The GPS time synchronization coordinates the multiple electrode pairs MN between different nodes and the power supply electrode pair AB to work simultaneously in parallel, realizing "one power supply and multiple measurements".

[0114] Again, the data acquisition process of the present invention can be carried out in a way of power supply while searching for potential measurement points. Although this method is feasible, its efficiency is too low, involving a large amount of repeated calculations and blind search processes, seriously affecting the data acquisition efficiency. Therefore, the acquisition efficiency can be improved by prefabricating the acquisition table in advance: after the sensing nodes are arranged once and the electrode point positions are fixed with accurate position coordinates, the power supply and potential measurement acquisition tables can be calculated and made in advance at the edge nodes, arranging the node numbers of the power supply points AB, the electrode numbers, and the corresponding node numbers and electrode numbers of the potential measurement points MN. There is a one-to-many relationship between power supply and potential measurement. Therefore, for a certain power supply electrode pair AB in the acquisition table, there are multiple potential measurement electrode pairs MN. The MN belonging to the same sensing node are placed one after another in columns, and the potential measurement points belonging to different sensing nodes are placed in rows according to the node numbers. During actual data acquisition, for each power supply point AB, extract the measurement electrode pair MN numbers from different nodes in column order, and simultaneously perform potential measurement in parallel. After the measurement is completed, save the node number, electrode number, acquisition time, and potential difference value. Then move to the next column in sequence, extract the measurement electrode pair MN numbers of different nodes corresponding to this column, notify the power supply point AB to supply power, and at the same time notify the corresponding electrodes in the nodes with the corresponding numbers to perform potential measurement and record and save. Then the pointer points to and extracts the MN electrode pair of the next column for potential measurement until the MN of the last column is empty, then the power supply process of this AB electrode pair is completed, move to the next power supply point in the acquisition table, and repeat the above process until the measurement of the last potential measurement point of the last power supply point in the acquisition table is completed, then the entire data acquisition process of this edge node is completed.

[0115] When there is an update of the sensing nodes (adding or deleting sensing nodes), submit the update information and recalculate the acquisition table for the updated measurement process. Using this table can greatly reduce the calculation workload and search time of the acquisition station and improve the acquisition efficiency.

[0116] 4. Upload and storage of measurement data

[0117] After the acquisition work is completed, the edge node notifies each sensing node to upload all the data of this work. The edge node sorts out the data uploaded by each sensing node in chronological order. The data uploaded by each node includes both power supply data and potential measurement data, which need to be compared with the acquisition table according to time to form a spreadsheet of edge node number, sensing node number, measurement time, power supply point A number, power supply point B number, measurement point M number, measurement point N number, power supply current I, potential difference V, device coefficient K, and apparent resistivity Ps. Among them, the device coefficient K and the apparent resistivity Ps are supplemented to the table after being calculated based on the position coordinates of ABMN, the power supply current I, and the potential difference V to form a complete measurement data information table.

[0118] 5. Data processing and data mining (artificial intelligence cloud computing)

[0119] Artificial intelligence for big data runs through the data flow process in the entire resistivity sensing system. From the competitive collaboration and optimal configuration of intelligent edge clouds based on federated computing, which forms automatic optimization management of the data acquisition process of sensing nodes, to data mining and intelligent analysis based on big data and machine learning in the central cloud, a sensing model is constructed to automatically and quickly identify anomalies.

[0120] The biggest feature of the present invention lies in the two-way feedback intelligent flow of data among the constituent units in the constructed system: the sensing nodes are controlled by the edge nodes, and at the same time, they automatically upload their own status information and the collected data to the edge nodes in a timely manner, facilitating the edge nodes to adjust the acquisition parameter settings and update the data acquisition frequency in a timely manner. The edge nodes report the preliminary processing and analysis results to the data center of the central cloud, and the data center distributes the model results of intelligent analysis based on historical data and other multi-source data to each edge node to guide each edge node to perform rapid anomaly analysis and risk identification. The urban brain receives the model prediction results and early warning information sent by the data center, and conducts scientific analysis and decision-making in combination with other multi-source data. At the same time, it also sends other multi-source data and their historical information back to the data center to help correct and improve the model.

[0121] 6. Multi-source data analysis and intelligent decision-making

[0122] Over time, the sensing system obtains a large amount of apparent resistivity data. The apparent resistivity is only a comprehensive reflection of the resistivity of the underground and spatial structures, and resistivity imaging results can only be obtained through resistivity inversion. Three-dimensional and four-dimensional resistivity imaging require a large amount of computing resources and machine time. Inversion of the overall data over a large range is neither economical nor realistic. Therefore, the present invention uses artificial intelligence algorithms in the cloud to perform intelligent analysis and data mining on resistivity big data, identify and discover the abnormal points and abnormal regions with large changes therein, and then perform fine four-dimensional inversion on the abnormal sections to understand the change characteristics of the abnormal sections over time and exclude the reasons such as weather factors. Then, the sensing measurement frequency of the key abnormal sections is increased. If the abnormal change shows a trend of acceleration or expansion of the scope, it enters the risk assessment mode: 1. Further encrypt the measurement frequency for dynamic real-time observation. 2. Conduct on-site verification and investigation, including on-site drilling verification and verification by other geophysical methods (radar, electromagnetic method or seismic exploration). If the on-site verification excludes the anomaly, analyze the reasons and modify the model and the alarm threshold for this section. If the on-site verification confirms the anomaly, report it to the urban brain, start multi-source data analysis and the expert system to judge the source and formation reason of the anomaly, and submit it to the decision-making and command system for emergency rescue and disposal. At the same time, use it as a positive successful case training data set to optimize the model and improve the prediction effect.

Claims

1. A method for collecting resistivity data of urban underground space based on cloud-edge-end collaboration, characterized in that The method specifically comprises the following steps: (1) Determine the layout and acquisition parameters of the resistivity sensing nodes based on the actual situation of the target street, the maximum exploration depth, and the resolution of the underground detection target; (2) Resistivity sensing nodes are arranged on the target streets. The central cloud computing platform assigns a unique system number to each edge node. The edge node assigns a unique system number to each resistivity sensing node in its domain. The sensing node assigns a unique system number to each electrode point in its system and collects the three-dimensional geographic coordinates of each electrode point. (3)The central cloud computing platform sequentially selects different edge nodes for block measurement. The selected edge node selects a sensing node as the power supply node in the order of the numbers of the resistivity sensing nodes, then selects an electrode combination within the sensing node as the power supply electrode pair AB, and selects the electrode combination within the edge node domain to which the sensing node belongs as the potential measurement electrode pair MN, and the potential measurement electrode pair MN belongs to the same sensing node; it is judged whether the distance between the measurement electrode pair MN and AB is within the effective measurement radius r of AB. If so, power supply and potential measurement are carried out; if not, move to the position of the next ABMN combination for a new measurement condition judgment; the effective measurement radius of AB , where n is the effective radius coefficient, n = 6 - 14, and a is the AB spacing; until all the power supply electrode pairs within the sensing node and the combinations of multiple paired potential measurement electrode pairs are traversed, the power supply and potential measurement processes when the sensing node serves as the power supply node are completed; (4) Move to the next resistivity sensing node in sequence to perform the power supply and potential measurement process until all power supply electrode combinations of the last sensing node are completed, thus completing the entire power supply and potential measurement process of the current edge node; (5) Then enter the next edge node and perform the same power supply and potential measurement process until all edge nodes are traversed; (6) After completing the collection work, the edge node notifies each sensing node to upload the collected data and its own status information. The edge node formats the data in the local area, quickly compares it with the model results of the local area downloaded from the central cloud computing platform, and gives the processing and analysis results; The edge nodes report the preliminary processing and analysis results to the central cloud computing platform. The central cloud computing platform distributes the model results feedback based on historical data and other multi-source data intelligent analysis to each edge node, guiding the subsequent edge nodes to conduct rapid anomaly analysis and risk identification; When the resistivity sensing nodes are arranged at one time, and the positions of the various electrode points are fixed and have accurate position coordinates, the edge node corresponding to the resistivity sensing node calculates and prepares the power supply and potential measurement acquisition table in advance, in which the sensing node number and electrode number of each power supply point AB and the corresponding sensing node number and electrode number of multiple potential measurement points MN are arranged in order, so that the actual acquisition is carried out in accordance with the order of the table to complete the entire data acquisition process; When AB is used as a power supply electrode pair, potential measurement electrode pairs MN that meet the conditions and are located between different sensing nodes are synchronized by the GPS module for collaborative measurement, that is, multiple potential measurement electrode pairs MN between different nodes work simultaneously with a power supply electrode pair AB to achieve one supply and multiple measurements.

2. The method for collecting resistivity data of urban underground space based on cloud-edge-end collaboration according to claim 1, wherein When selecting the power supply electrode pair, follow the principle of the electrode number from small to large, starting from the end where the collection station is located, taking the electrode point closest to the collection station as electrode A, and selecting the electrode point with the AB sequence number interval equal to 1 as electrode B for power supply; then keep the AB sequence number interval, move A and B to the next electrode point, until point B reaches the last electrode point of the current sensing node, and complete the power supply process of all AB sequence number intervals equal to 1; Then, starting from the starting point, select the measuring point with 2 serial number intervals between AB to implement power supply, and then move A and B in sequence until point B reaches the last electrode point, thus completing the power supply process with 2 serial number intervals between AB; The AB interval is changed repeatedly until the set maximum isolation coefficient is reached, and the power supply process of the sensing node is completed.

3. A resistivity sensing system for urban underground space based on cloud-edge-terminal collaboration, characterized in that, This system is used to implement the method for collecting resistivity data of urban underground space based on cloud-edge-terminal collaboration described in any one of claims 1 to 2; the system adopts a cloud-edge-terminal architecture design, including a central cloud computing platform, multiple edge servers connected to the central cloud computing platform through a distributed network, and multiple resistivity sensing nodes connected to each edge server through a distributed network; The central cloud computing platform is used to manage the entire resistivity sensing system, including: setting up and configuring distributed edge servers, and managing all resistivity sensing nodes through the edge servers; performing global data processing and model inversion, including comparing and mining real-time data and historical data, and sending the model results to the edge servers to guide preliminary data analysis; alarming and reporting data anomalies exceeding the threshold; The edge server is an edge node, which is used to control multiple resistivity sensing nodes within its sharded control domain to work collaboratively, including: calculating and making power supply and potential measurement acquisition tables, arranging the node numbers and electrode numbers of the power supply points AB, and the node numbers and electrode numbers of the corresponding potential measurement points MN; coordinating and controlling the selection and acquisition process of power supply and potential measurement electrode pairs within the domain; screening, sorting and storing the data obtained within the domain in the designed format, and at the same time uploading the data to the central cloud computing platform for backup; after data acquisition is completed, comparing and analyzing the real-time data with the historical data and the model calculation results of this area feedback by the central cloud computing platform to the edge node to judge whether there are anomalies; when there are abnormal changes, reporting the abnormal information to the central cloud computing platform; The resistivity sensing node is an end node, and multiple resistivity sensing nodes are arranged horizontally along urban roads and / or arranged in vertical wells; each resistivity sensing node is an independent resistivity sensor unit, which includes a collection station, a multi-channel electrode conversion switch connected to the collection station, a multi-core high-density electrical method cable, and a grounding electrode connected to the multi-core high-density electrical method cable; the resistivity sensing node respectively executes power supply or potential measurement tasks according to the instruction requirements of its affiliated edge node, and uploads the measurement data to the corresponding edge node; When the resistivity sensing nodes are arranged horizontally along urban roads, the cable in the resistivity sensing node is a multi-core cascaded high-density electrical method cable, and the cascaded cable is connected in series into a whole cable through a cascaded electrode conversion switch, and the collection station is connected to the end of the whole cable; When the resistivity sensing nodes are arranged in vertical wells, the cable in the resistivity sensing node is a single centralized high-density electrical method well cable, and multiple electrode junctions are equidistantly arranged on the cable, and each electrode junction serves as a grounding electrode, and the top of the cable is connected to the collection station through a centralized electrode switch; When the resistivity sensing nodes are arranged horizontally along urban roads and in combination with well holes, a single centralized high-density electrical method cable arranged in the well hole is first connected to one end of a multi-core segmented cascaded high-density electrical method cable on the ground through a centralized electrode conversion switch, and the acquisition station is connected to the other end of the segmented cascaded high-density electrical method cable; and a plurality of electrode nodes are arranged at equal intervals on the centralized high-density electrical method cable, and each electrode node serves as a grounding electrode; The acquisition station includes a control module, a power supply module, a potential measurement module, a communication module and a GPS module; Under the command of the edge node to which it belongs, the control module controls the other several modules of this acquisition station to realize the operation management, self-check, communication with the edge node, and function interchange of power supply / potential measurement, channel selection, acquisition process execution, and data storage and upload of measurement data under the control of the acquisition instruction of the acquisition station itself; After receiving the power supply instruction, the power supply module selects the corresponding electrode channel through the control module and supplies power to the ground through the cable channel and electrode connected thereto, and at the same time measures the magnitude of the supplied current. After the power supply is completed, it uploads the node number, its power supply channel number, the measurement start time, and the supplied current value; After receiving the potential measurement instruction, the potential measurement module selects the corresponding electrode channel through the control module and measures the potential through the cable channel and electrode connected thereto, and at the same time measures the magnitude of the potential difference; after the measurement is completed, it uploads the node number, its potential measurement channel number, the measurement start time, and the potential difference value; The GPS module is used for precise timing and coordination of each node.

4. The well - to - surface joint resistivity sensing system based on cloud - edge - device collaboration according to claim 3, wherein, The edge node and the end node perform remote data transmission through a mobile communication network, and the edge node and the central cloud computing platform perform remote data transmission through a wired network.

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