Side slope radar early warning system and method based on domain network

Abstract

The invention discloses a slope radar early warning system and method based on a domain network, and relates to the technical field of slope safety monitoring. The system comprises a data exporting and obtaining module, a domain network communication connection module, a receiving server remote connection module and an early warning information exporting module, it is ensured that radar data is uploaded to a public network server in real time, and private network communication, multi-user simultaneous access and management and timely and accurate pushing of early warning information of domain networks among multiple regions are achieved. The method comprises the steps of measuring slope operation parameters in real time, calculating a stability index and a comprehensive index, carrying out fault early warning and recognition, and dynamically adjusting a mechanism. Data are collected through various sensors, a stability index and a comprehensive index are calculated, fault identification is carried out based on a historical database and feature vectors, and a monitoring period and threshold setting are optimized through a dynamic adjustment mechanism. According to the invention, the accuracy and timeliness of early warning are improved, the self-adaptive capability of the system is optimized, potential risks can be quickly positioned and processed, and the safety of the slope is ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of slope safety monitoring, and in particular relates to a slope radar early warning system and method based on a domain network. Background Art

[0002] In the process of open-pit metal mining, accurate slope radar monitoring and early warning are the key to ensuring production safety and improving mining efficiency. We must attach great importance to slope monitoring in open-pit mining engineering activities to ensure the safety of open-pit mining engineering activities. However, traditional slope radars all adopt C / S architecture, and only display monitoring data and early warning information in analysis software. It is difficult to integrate multiple types of slope radars in multiple regions into the monitoring cloud platform of B / S architecture. The present invention aims to synchronize slope radar early warnings and data in the domain network to the Web platform, and realize real-time, accurate and efficient online monitoring services of slope radars in multiple regions.

[0003] Traditional open-pit mine slope radars all adopt the C / S architecture in the domain network environment. After the radar data is collected, it is transmitted to the local data server. After the radar solution software corrects the atmospheric refractive index, shielding area and other parameters, it is stored in the database. However, the displacement value, velocity, acceleration, consistency and other data of each pixel point can only be displayed in the radar software of the local server. In order to display radar monitoring data to more users through the cloud platform and push warning messages synchronously, and realize the slope radar warning function of multiple sets in multiple regions, this method adopts NSSM encapsulation Windows service technology, VPN virtual private network technology and FreeRDP remote desktop Web application technology to synchronize the slope radar software data in the domain network to the cloud platform server, analyze and push warning messages according to the warning category, and finally integrate the remote desktop on the Web end to form a mine slope radar warning and data synchronization method, which provides a solution for the development of a comprehensive warning platform for mine monitoring and effectively guarantees the safe and stable operation of open-pit mines. Summary of the invention

[0004] In order to overcome the shortcomings and deficiencies of the above-mentioned prior art, the first purpose of the present invention is to provide a slope radar early warning system based on a domain network; the second purpose of the present invention is to provide a slope radar early warning method based on a domain network.

[0005] The first object of the present invention adopts the following technical solution: A slope radar early warning system based on a domain network, comprising a data export and acquisition module, a domain network communication connection module, a receiving server remote connection module and an early warning information export module; Data export and acquisition module: The slope radar software periodically exports a character-separated value file containing coordinates, displacement values, and warning information to the storage path specified by the local server; the scheduled scanning program is written in Java language, the scanning frequency is configured to match the slope radar scanning frequency, and the data forwarding program is encapsulated as a Windows service using NSSM to ensure that the data file is sent to the server with a fixed IP address on the public network in a timely manner after it is generated; Domain network communication connection module: Install SoftEther VPN Server management tool on the receiving server, create a virtual Hub, set up a VPN virtual private network service, configure the listener TCP / IP port, and map it to the public network through the router's NAT function; create VPN login accounts for the monitoring radar data server, domain controller, and receiving server, and install SoftEther VPN Client management tool on the above servers, install virtual network adapters and configure VPN connections to achieve private network communication between domain networks in multiple regions; Receiving server remote connection module: Add multiple domain users to the domain controller of the radar network, add the accounts to the remote desktop user group of the receiving server, enable the remote desktop authorization management service, and enable multiple users to use remote desktop access at the same time; install the FreeRDP-WebConnect component, configure the remote desktop http and https protocol access ports, and add them to the Windows service. Finally, embed the Web remote port into the early warning platform through the iframe tag to enable the remote desktop function on the web page; Warning information export module: According to the warning message export function of the radar alarm software, the file is interpreted, and the message containing the alarm identifier, timestamp and radar number is integrated and sent to the web front end. The monitoring personnel will review the warning message content and then push it.

[0006] The second object of the present invention adopts the following technical solution: A slope radar early warning method based on a domain network is used to implement a slope radar early warning system based on a domain network. The method steps are as follows: Step 1: Real-time measurement and storage of various operating parameters of the slope area; the parameters include radar reflection signal strength, ground displacement, soil moisture, rainfall, temperature and wind speed; Step 2: Based on the operating parameters, calculate the short-term state indicators of the slope and the stability index of the slope; the short-term state indicators include ground displacement amplitude, ground displacement duration, soil moisture change rate and temperature change rate; Step 3: Based on the operating parameters, calculate the long-term state indicators of the slope and the comprehensive index of the slope; the long-term state indicators include the average ground displacement, soil moisture change trend, rainfall accumulation and temperature change trend within the statistical period T; Step 4: Perform slope fault warning based on stability index and comprehensive index; Step 5: Perform slope fault identification based on historical database, short-term status indicators, and long-term status indicators; Step 6: Establish a dynamic adjustment mechanism.

[0007] Preferably, in step one, the radar reflection signal strength is obtained by a radar device; the ground displacement is obtained by a radar device or a GPS device; the soil moisture is obtained by a soil moisture sensor; the rainfall is obtained by a rain gauge; the temperature is obtained by a temperature sensor; and the wind speed is obtained by a wind speed sensor.

[0008] Preferably, step one also includes configuring a historical database for storing historical data for data analysis and fault identification.

[0009] Preferably, step 2 is as follows: The amplitude of each ground displacement per unit time is counted and the average is calculated to obtain the ground displacement amplitude; Count the total duration of ground displacement per unit time to obtain the ground displacement duration; Calculate the rate of change of soil moisture per unit time to obtain the soil moisture change rate; Calculate the rate of change of temperature per unit time to obtain the temperature change rate; The stability index is calculated according to the formula, which is as follows: ; in, represents the stability index; Indicates A short-term status indicator; Indicates The benchmark value of a short-term status indicator; Indicates The standard deviation of the short-term state indicator; Indicates The weight coefficient of a short-term state indicator.

[0010] As a preference, step three is specifically as follows: Calculate the mean value of the ground displacement within a statistical period to obtain the average ground displacement; Calculate the soil moisture variation trend within a statistical period to obtain the soil moisture variation trend; Calculate the cumulative rainfall in a statistical period to obtain the cumulative rainfall; Calculate the temperature change trend within a statistical period to obtain the temperature change trend; The comprehensive index is calculated according to the formula, which is as follows: ; in, represents the composite index; Indicates A short-term status indicator; Indicates The benchmark value of a long-term status indicator; Indicates The standard deviation of the long-term status indicator; Indicates The weight coefficient of the long-term status indicator.

[0011] As a preference, step four is specifically as follows: Monitor short-term status indicators and stability indexes; Set the threshold range for each short-term status indicator and the stability threshold of the stability index; When any short-term status indicator exceeds the corresponding threshold range or the stability index is lower than the stability threshold, an early warning message is generated; Monitor long-term status indicators and composite indices; Set the threshold range for each long-term status indicator and the composite threshold for the composite index; When any long-term status indicator exceeds the corresponding threshold range or the comprehensive index is higher than the comprehensive threshold, an early warning message is generated; The warning information includes abnormal data and its corresponding timestamp.

[0012] As a preference, step five is specifically as follows: S51, generating a state characteristic vector of the slope according to the state of the slope; Extract relevant parameters corresponding to the early warning information, including short-term status indicators, long-term status indicators and control parameters. The control parameters include soil type, slope and geological structure. Standardize the relevant parameters, encode each relevant parameter after the standardization into a feature vector, and concatenate all the feature vectors into a state feature vector of the slope; S52, performing slope fault identification; Read historical feature vectors from the historical database; Calculate the similarity between the state feature vector and each historical feature vector; Extracting historical feature vectors whose similarity is greater than a preset similarity threshold; Extract the slope fault type corresponding to each matching vector and count the number of occurrences of each fault type; The fault type that occurs most frequently is taken as the result of fault identification.

[0013] As a preference, step six is ​​specifically as follows: S61, stability index adjustment; First adjustment threshold: when the stability index is lower than the first adjustment threshold, the first monitoring unit sends a period adjustment instruction to the long-term indicator unit; Cycle adjustment strategy: The long-term indicator unit responds to the cycle adjustment instruction and adjusts the cycle length of the statistical cycle based on the adjustment strategy of the statistical cycle; S62, comprehensive index adjustment; Second adjustment threshold: when the comprehensive index is higher than the second adjustment threshold, the second monitoring unit sends a threshold adjustment instruction to the first monitoring unit; Threshold adjustment strategy: the first monitoring unit responds to the threshold adjustment instruction and adjusts the stability threshold of the stability index based on the stability threshold adjustment strategy; S63, historical database update; Each piece of historical data includes a historical feature vector, a timestamp corresponding to the historical feature vector, and a fault type of the slope; Each time a slope fault is identified, the historical database is updated, and the state feature vector involved in the fault identification is used as a historical feature vector. The fault type and timestamp corresponding to the result of the fault identification together form a historical data and are added to the historical database.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention builds private network communication through SoftEther VPN technology, configures the listener TCP / IP port and maps it to the public network through the NAT function of the router to ensure the security and reliability of data transmission; it ensures the security and stability of data during transmission, prevents data from being tampered with or leaked, and improves the overall security of the system.

[0015] 2. The present invention calculates the stability index and comprehensive index, combines short-term and long-term status indicators to perform fault warning, and optimizes the monitoring cycle and threshold settings through a dynamic adjustment mechanism, thereby improving the accuracy and timeliness of the warning and being able to discover and handle potential risks in a timely manner. At the same time, through the dynamic adjustment mechanism, the system can adaptively optimize the monitoring strategy, improving the intelligence level and adaptive ability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1A module diagram of a slope radar early warning system based on a domain network of the present invention is shown; Figure 2 A flow chart of a slope radar early warning method based on a domain network of the present invention is shown; Figure 3 A flow chart of the dynamic adjustment mechanism of the present invention is shown. DETAILED DESCRIPTION

[0018] 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.

[0019] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0020] Embodiment 1: See also Figure 1 As shown, a slope radar warning system based on a domain network in this embodiment includes a data export and acquisition module, a domain network communication connection module, a receiving server remote connection module and a warning information export module.

[0021] Data export and acquisition module: The slope radar software periodically exports character-separated value (.csv) files containing coordinates, displacement values, and warning information to the storage path specified by the local server; a timed scanning program is written in Java language, the scanning frequency is configured to match the slope radar scanning frequency, and the data forwarding program is encapsulated as a Windows service using NSSM to ensure that data is sent to the server with a fixed IP address on the public network in a timely manner after the data file is generated.

[0022] Domain network communication connection module: Install the SoftEther VPN Server management tool on the receiving server, create a virtual Hub, set up a VPN virtual private network service, configure the listener TCP / IP port, and map it to the public network through the router's NAT function; create VPN login accounts for the monitoring radar data server, domain controller, and receiving server, and install the SoftEther VPN Client management tool on the above servers, install a virtual network adapter and configure a VPN connection to achieve private network communication between domain networks in multiple regions.

[0023] Receiving server remote connection module: Add multiple domain users to the domain controller of the radar network, add these accounts to the remote desktop user group of the receiving server, enable the remote desktop authorization management service, and enable multiple users to use remote desktop access at the same time; install the FreeRDP-WebConnect component, configure the remote desktop http and https protocol access ports, and add them to the Windows service. Finally, embed the Web remote port into the early warning platform through the iframe tag to enable the remote desktop function on the web page.

[0024] Warning information export module: According to the warning message export function of the radar alarm software, the file is interpreted, and the message containing the alarm identifier, timestamp and radar number is integrated and sent to the web front end. The monitoring personnel will review the warning message content and then push it.

[0025] The beneficial effects of this embodiment are as follows: through the timed scanning and data forwarding program, the radar data is ensured to be uploaded to the public network server in real time, thereby improving the timeliness of data processing; the private network communication is constructed by using SoftEther VPN technology to ensure the security and reliability of data transmission; through the remote desktop and Web remote access functions, multi-user simultaneous access and management are realized, thereby improving the usability and maintenance efficiency of the system; the warning information is interpreted and integrated, thereby ensuring that the monitoring personnel can obtain and process the warning information in a timely and accurate manner, thereby improving the accuracy and effectiveness of the warning.

[0026] Embodiment 2: See also Figure 2 As shown, a slope radar early warning method based on a domain network in this embodiment is as follows: Step 1: Measure various operating parameters of the slope area in real time and store them.

[0027] Parameters include radar reflection signal strength, ground displacement, soil moisture, rainfall, temperature, wind speed, etc.

[0028] Radar reflection signal strength: The reflection signal strength of the slope surface is obtained through radar equipment.

[0029] Ground displacement: Get ground displacement data through radar equipment or GPS equipment.

[0030] Soil moisture: Get soil moisture data through the soil moisture sensor.

[0031] Rainfall: Get rainfall data through a rain gauge.

[0032] Temperature: Get ambient temperature data through the temperature sensor.

[0033] Wind speed: Get wind speed data through wind speed sensor.

[0034] The collected operating parameters are stored and configured with a historical database.

[0035] Historical database: stores historical data for subsequent data analysis and fault identification.

[0036] Step 2: Calculate the stability index.

[0037] Based on the operating parameters, the short-term state indicators of the slope are calculated, and the stability index of the slope is calculated.

[0038] Short-term status indicators include ground displacement amplitude, ground displacement duration, soil moisture change rate, temperature change rate, etc.

[0039] Ground displacement amplitude: Count the amplitude of each ground displacement per unit time and calculate the average.

[0040] Ground displacement duration: the total duration of ground displacement per unit time.

[0041] Soil moisture change rate: Calculate the rate of change of soil moisture per unit time.

[0042] Temperature change rate: Calculate the rate of change of temperature per unit time.

[0043] The stability index is calculated according to the formula, which is as follows: ; in, represents the stability index; Indicates A short-term status indicator; Indicates The benchmark value of a short-term status indicator; Indicates The standard deviation of the short-term state indicator; Indicates The weight coefficient of a short-term state indicator.

[0044] Step 3: Calculate the comprehensive index.

[0045] Based on the operating parameters, the long-term state indicators of the slope are calculated, and the comprehensive index of the slope is calculated.

[0046] Set the statistical period T and calculate the long-term status indicators, including average ground displacement, soil moisture change trend, rainfall accumulation, temperature change trend, etc.

[0047] Average ground displacement: Calculate the average ground displacement within a statistical period.

[0048] Soil moisture change trend: Calculate the soil moisture change trend within a statistical period.

[0049] Accumulated rainfall: Calculate the accumulated rainfall within a statistical period.

[0050] Temperature change trend: Calculate the temperature change trend within a statistical period.

[0051] The comprehensive index is calculated according to the formula, which is as follows: ; in, represents the composite index; Indicates A short-term status indicator; Indicates The benchmark value of a long-term status indicator; Indicates The standard deviation of the long-term status indicator; Indicates The weight coefficient of the long-term status indicator.

[0052] Step 4: Perform slope fault warning based on stability index and comprehensive index.

[0053] Monitor short-term status indicators and stability indexes; Set the threshold range for each short-term status indicator and the stability threshold for the stability index; When any short-term status indicator exceeds the corresponding threshold range or the stability index is lower than the stability threshold, an early warning message is generated.

[0054] Monitor long-term status indicators and composite indices; Set the threshold range for each long-term status indicator and the composite threshold for the composite index; When any long-term status indicator exceeds the corresponding threshold range or the comprehensive index is higher than the comprehensive threshold, a warning message is generated.

[0055] Warning information: includes abnormal data and its corresponding timestamp.

[0056] Step 5: Identify slope faults based on historical database, short-term status indicators, and long-term status indicators.

[0057] S51. Generate a state characteristic vector of the slope according to the state of the slope.

[0058] Extract relevant parameters corresponding to the early warning information, including short-term status indicators, long-term status indicators and control parameters (such as soil type, slope, geological structure, etc.); The relevant parameters are standardized, each of the standardized relevant parameters is encoded into a feature vector, and all the feature vectors are concatenated into a state feature vector of the slope.

[0059] S52. Perform slope fault identification.

[0060] Read historical feature vectors from the historical database; Calculate the similarity between the state feature vector and each historical feature vector; Extracting historical feature vectors whose similarity is greater than a preset similarity threshold; Extract the slope fault type corresponding to each matching vector and count the number of occurrences of each fault type; The fault type that occurs most frequently is taken as the result of fault identification.

[0061] Step 6: Dynamic adjustment mechanism.

[0062] See also Figure 3 As shown, the steps of the dynamic adjustment mechanism are as follows: S61. Stability index adjustment.

[0063] First adjustment threshold: When the stability index is lower than the first adjustment threshold, the first monitoring unit sends a period adjustment instruction to the long-term indicator unit.

[0064] Cycle adjustment strategy: The long-term indicator unit responds to the cycle adjustment instruction and adjusts the cycle length of the statistical cycle based on the adjustment strategy of the statistical cycle.

[0065] S62. Adjustment of comprehensive index.

[0066] Second adjustment threshold: When the comprehensive index is higher than the second adjustment threshold, the second monitoring unit sends a threshold adjustment instruction to the first monitoring unit.

[0067] Threshold adjustment strategy: The first monitoring unit responds to the threshold adjustment instruction and adjusts the stability threshold of the stability index based on the stability threshold adjustment strategy.

[0068] S63. Historical database update.

[0069] Each piece of historical data includes a historical feature vector, a timestamp corresponding to the historical feature vector, and a fault type of the slope.

[0070] Each time the fault identification module performs a slope fault identification, the data storage module updates the historical database, takes the state feature vector involved in the fault identification as the historical feature vector, and the fault type and timestamp corresponding to the result of the fault identification together form a historical data and add it to the historical database.

[0071] The beneficial effects of this embodiment are: real-time collection of slope operating parameters through multiple sensors to ensure the comprehensiveness and accuracy of data; timely generation of early warning information based on dynamic adjustment of stability index and comprehensive index to improve the accuracy and timeliness of early warning; combined with historical database and feature vector, fault type identification is carried out to help quickly locate and deal with potential risks; through the dynamic adjustment mechanism of stability index and comprehensive index, the monitoring cycle and threshold setting are optimized to enhance the adaptive ability of the system.

[0072] The weight factors of the present invention are used to measure the degree of influence of different factors or variables on a certain result or decision. The definition of weight factor refers to the value assigned to each factor when comparing and evaluating multiple factors to reflect its importance or priority. These weight factors can be determined according to specific circumstances and needs, and are usually jointly formulated and confirmed by professionals or relevant stakeholders. By reasonably setting the weight factors, it can help the program or system to make decisions or predictions more accurately.

[0073] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0074] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A slope radar early warning system based on a domain network, characterized in that: The system includes a data export and acquisition module, a domain network communication connection module, a receiving server remote connection module and an early warning information export module; Data export and acquisition module: The slope radar software periodically exports a character-separated value file containing coordinates, displacement values, and warning information to a storage path specified by the local server; Use Java language to write a timed scanning program, configure the scanning frequency to match the slope radar scanning frequency, and use NSSM to encapsulate the data forwarding program as a Windows service to ensure that the data file is sent to the public network fixed IP server in a timely manner after it is generated; Domain network communication connection module: Install SoftEther VPN Server management tool on the receiving server, create a virtual Hub, set up a VPN virtual private network service, configure the listener TCP / IP port, and map it to the public network through the router's NAT function; create VPN login accounts for the monitoring radar data server, domain controller, and receiving server, and install SoftEther VPN Client management tool on the above servers, install virtual network adapters and configure VPN connections to achieve private network communication between domain networks in multiple regions; Receiving server remote connection module: Add multiple domain users to the domain controller of the radar network, add the accounts to the remote desktop user group of the receiving server, enable the remote desktop authorization management service, and enable multiple users to use remote desktop access at the same time; install the FreeRDP-WebConnect component, configure the remote desktop http and https protocol access ports, and add them to the Windows service. Finally, embed the Web remote port into the early warning platform through the iframe tag to enable the remote desktop function on the web page; Warning information export module: According to the warning message export function of the radar alarm software, the file is interpreted, and the message containing the alarm identifier, timestamp and radar number is integrated and sent to the web front end. The monitoring personnel will review the warning message content and then push it.

2. A slope radar early warning method based on a domain network, used to implement a slope radar early warning system based on a domain network as claimed in claim 1, characterized in that: The method steps are as follows: Step 1: Real-time measurement and storage of various operating parameters of the slope area; the parameters include radar reflection signal strength, ground displacement, soil moisture, rainfall, temperature and wind speed; Step 2: Based on the operating parameters, calculate the short-term state indicators of the slope and the stability index of the slope; the short-term state indicators include ground displacement amplitude, ground displacement duration, soil moisture change rate and temperature change rate; Step 3: Based on the operating parameters, calculate the long-term state index of the slope and calculate the comprehensive index of the slope; Long-term status indicators include average ground displacement, soil moisture change trend, rainfall accumulation and temperature change trend within the statistical period T; Step 4: Perform slope fault warning based on stability index and comprehensive index; Step 5: Perform slope fault identification based on historical database, short-term status indicators, and long-term status indicators; Step 6: Establish a dynamic adjustment mechanism.

3. The slope radar early warning method based on domain network according to claim 2 is characterized in that: In the step 1, the radar reflection signal strength is obtained by a radar device; the ground displacement is obtained by a radar device or a GPS device; the soil moisture is obtained by a soil moisture sensor; the rainfall is obtained by a rain gauge; the temperature is obtained by a temperature sensor; and the wind speed is obtained by a wind speed sensor.

4. The slope radar early warning method based on domain network according to claim 2 is characterized in that: The step 1 also includes configuring a historical database for storing historical data for data analysis and fault identification.

5. The slope radar early warning method based on domain network according to claim 2 is characterized in that: The step 2 is specifically as follows: The amplitude of each ground displacement per unit time is counted and the average is calculated to obtain the ground displacement amplitude; Count the total duration of ground displacement per unit time to obtain the ground displacement duration; Calculate the rate of change of soil moisture per unit time to obtain the soil moisture change rate; Calculate the rate of change of temperature per unit time to obtain the temperature change rate; The stability index is calculated according to the formula, which is as follows: ； in, represents the stability index; Indicates A short-term status indicator; Indicates The benchmark value of a short-term status indicator; Indicates The standard deviation of the short-term state indicator; Indicates The weight coefficient of a short-term state indicator.

6. The slope radar early warning method based on domain network according to claim 2 is characterized in that: The step three is as follows: Calculate the mean value of the ground displacement within a statistical period to obtain the average ground displacement; Calculate the soil moisture variation trend within a statistical period to obtain the soil moisture variation trend; Calculate the cumulative rainfall in a statistical period to obtain the cumulative rainfall; Calculate the temperature change trend within a statistical period to obtain the temperature change trend; The comprehensive index is calculated according to the formula, which is as follows: ； in, represents the composite index; Indicates A short-term status indicator; Indicates The benchmark value of a long-term status indicator; Indicates The standard deviation of the long-term status indicator; Indicates The weight coefficient of the long-term status indicator.

7. The slope radar early warning method based on domain network according to claim 2 is characterized in that: The step 4 is specifically as follows: Monitor short-term status indicators and stability indexes; Set the threshold range for each short-term status indicator and the stability threshold for the stability index; When any short-term status indicator exceeds the corresponding threshold range or the stability index is lower than the stability threshold, an early warning message is generated; Monitor long-term status indicators and composite indices; Set the threshold range for each long-term status indicator and the composite threshold for the composite index; When any long-term status indicator exceeds the corresponding threshold range or the comprehensive index is higher than the comprehensive threshold, an early warning message is generated; The warning information includes abnormal data and its corresponding timestamp.

8. The slope radar early warning method based on domain network according to claim 2 is characterized in that: The step five is as follows: S51, generating a state characteristic vector of the slope according to the state of the slope; Extract relevant parameters corresponding to the warning information, including short-term status indicators, long-term status indicators and control parameters. The control parameters include soil type, slope and geological structure. Standardize the relevant parameters, encode each relevant parameter after the standardization into a feature vector, and concatenate all the feature vectors into a state feature vector of the slope; S52, performing slope fault identification; Read historical feature vectors from the historical database; Calculate the similarity between the state feature vector and each historical feature vector; Extracting historical feature vectors whose similarity is greater than a preset similarity threshold; Extract the slope fault type corresponding to each matching vector and count the number of occurrences of each fault type; The fault type that occurs most frequently is taken as the result of fault identification.

9. The slope radar early warning method based on domain network according to claim 2 is characterized in that: The step six is ​​as follows: S61, stability index adjustment; First adjustment threshold: when the stability index is lower than the first adjustment threshold, the first monitoring unit sends a period adjustment instruction to the long-term indicator unit; Cycle adjustment strategy: The long-term indicator unit responds to the cycle adjustment instruction and adjusts the cycle length of the statistical cycle based on the adjustment strategy of the statistical cycle; S62, comprehensive index adjustment; Second adjustment threshold: when the comprehensive index is higher than the second adjustment threshold, the second monitoring unit sends a threshold adjustment instruction to the first monitoring unit; Threshold adjustment strategy: the first monitoring unit responds to the threshold adjustment instruction and adjusts the stability threshold of the stability index based on the stability threshold adjustment strategy; S63, historical database update; Each piece of historical data includes a historical feature vector, a timestamp corresponding to the historical feature vector, and a fault type of the slope; Each time a slope fault is identified, the historical database is updated, and the state feature vector involved in the fault identification is used as a historical feature vector. The fault type and timestamp corresponding to the result of the fault identification together form a historical data and are added to the historical database.

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