Geological disaster life detection equipment performance evaluation and display system

Abstract

The present application relates to geological disaster rescue technical field, disclose a kind of geological disaster life detection equipment efficiency evaluation and display system, including sensing acquisition module, for combining bottom layer sensor sensing message generation aggregation data stream;Positioning tracking module, for analyzing three-dimensional coordinate point set generation personnel and equipment location information;Efficiency evaluation module, according to aggregation data stream and preset evaluation index execution analytic hierarchy process calculation, generate equipment comprehensive efficiency score;Environment display module, for superimposed display equipment comprehensive efficiency score in preset three-dimensional electronic map.The present application realizes efficiency evaluation full-process automation and mass data unified management, solves the present artificial evaluation cumbersome error problem, provides intuitive three-dimensional visual presentation and scientific objective evaluation result.

Description

Technical Field

[0001] This invention relates to the field of geological disaster relief technology, and in particular to a system for evaluating and demonstrating the effectiveness of geological disaster life detection equipment. Background Technology

[0002] Sudden geological disasters such as mudslides and landslides are characterized by vast affected areas, a large number of trapped people, urgent rescue time, and complex search and rescue environments. After a disaster, using substandard search and rescue equipment or adopting inappropriate search and rescue methods can easily lead to missed rescue opportunities. In recent years, with the continuous investment in the construction of emergency rescue systems, a large number of new rescue equipment for buried personnel have been developed and put into front-line use. In order to accurately measure the ability of relevant equipment to complete the established search and rescue tasks, it is objectively necessary to conduct rigorous performance evaluation.

[0003] The current evaluation process is quite complex. Evaluators need to set up a multi-dimensional evaluation index system and formulate scoring standards in advance, and collect various measured hardware parameters according to on-site testing specifications. Then, the acquired basic data is input into a matrix to perform multiple rounds of calculations before a comprehensive evaluation score can be obtained. In the data calculation and transfer process, relying on manual processing of massive amounts of information is not only time-consuming and prone to calculation errors, but also fails to achieve unified storage of massive surveying and mapping data, historical information retrieval and traceability, and secure archiving.

[0004] To address the complex computational challenges of basic data, conventional operations often rely on specialized mathematical analysis software to perform background calculations. However, this software has a high learning curve and overly abstract user interfaces, hindering its widespread adoption among evaluation experts outside of programming fields. Furthermore, general-purpose mathematical analysis software cannot integrate complex equipment background information, multi-source expert scoring documents, and field-measured indicators to build customized structural management frameworks, nor can it directly generate and output standardized comprehensive evaluation reports. The industry urgently needs to develop a comprehensive system software with an intuitive interface, ease of use, and the ability to manage indicators throughout their entire lifecycle. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a system for evaluating and demonstrating the effectiveness of geological disaster life detection equipment.

[0006] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows: This invention provides a system for evaluating and demonstrating the effectiveness of geological disaster life detection equipment, comprising: The underlying sensors are deployed at the test site to collect the environmental conditions and send out raw sensing messages. Target positioning tags are configured on the personnel and equipment being inspected to provide basic location data; The display terminal and communication connection environment display module are used to provide a visual display interface. The sensing and acquisition module connects to the underlying sensors, receives the raw sensing messages sent by the underlying sensors, combines the raw sensing messages to generate an aggregated data stream, and transmits the aggregated data stream to the performance evaluation module. The positioning and tracking module acquires the set of three-dimensional coordinate points corresponding to the target positioning tag, parses the three-dimensional coordinate point set to generate the location information of the personnel being detected and the location information of the equipment being detected, and transmits the location information of the personnel being detected and the location information of the equipment being detected to the environment display module. The performance evaluation module receives the aggregated data stream transmitted by the sensing and acquisition module, reads the preset evaluation index parameters, performs hierarchical analysis calculations based on the aggregated data stream and the preset evaluation index parameters, generates the equipment comprehensive performance score, and transmits the equipment comprehensive performance score to the environment display module. The environment display module receives the location information of the personnel being tested and the location information of the equipment being tested transmitted by the positioning and tracking module, extracts the preset 3D electronic map and renders the preset 3D electronic map on the display terminal, obtains the comprehensive equipment performance score transmitted by the performance evaluation module, and overlays and displays the comprehensive equipment performance score on the coordinate layer corresponding to the location information of the equipment being tested within the preset 3D electronic map.

[0007] Furthermore, the performance evaluation module of the geological disaster life detection equipment performance evaluation and demonstration system of the present invention includes: The expert management submodule collects the identity information of the experts participating in the scoring and the qualitative indicator rating data, and sends the qualitative indicator rating data to the manual evaluation submodule. The indicator management submodule stores the preset performance evaluation indicator system, receives modification instructions that apply to the preset performance evaluation indicator system, and outputs preset evaluation indicator parameters to the matching evaluation submodule according to the modification instructions. The matching evaluation submodule receives the preset evaluation index parameters output by the index management submodule, performs hierarchical analysis and entropy weight calculation based on the preset evaluation index parameters, generates a comprehensive weight matrix, and sends the comprehensive weight matrix to the report output submodule. The manual evaluation submodule is equipped with an input interface. It receives qualitative indicator rating data sent by the expert management submodule, obtains quantitative indicator test results entered through the input interface, integrates the qualitative indicator rating data and the quantitative indicator test results to generate equipment data verification information, and sends the equipment data verification information to the report output submodule. The report output submodule receives the comprehensive weight matrix sent by the matching evaluation submodule and the equipment data verification information sent by the manual evaluation submodule. Based on the comprehensive weight matrix and the equipment data verification information, it performs matrix multiplication calculations to generate the final comprehensive performance document.

[0008] Furthermore, in the geological disaster life detection equipment effectiveness evaluation and demonstration system of the present invention, the matching evaluation submodule is configured as follows: Extract the preset evaluation index parameters and construct a multi-level structured judgment matrix based on the preset evaluation index parameters; Perform matrix eigenvalue calculation to generate the initial weights for each index within a multi-level structured judgment matrix; Based on the initial weights and the largest eigenvalue of the multi-level structure judgment matrix, calculate the consistency ratio of the multi-level structure judgment matrix and determine whether the consistency ratio is less than the preset consistency test threshold. When the consistency ratio is less than the preset consistency test threshold, the preset entropy weight algorithm formula is retrieved to process the multi-level structure judgment matrix and the initial weights, generate the comprehensive index weight values, and construct the comprehensive index weight matrix.

[0009] Furthermore, the geological disaster life detection equipment performance evaluation and demonstration system of the present invention integrates a multi-level fuzzy comprehensive evaluation engine within its manual evaluation sub-module. The multi-level fuzzy comprehensive evaluation engine is configured as follows: Integrate quantitative indicator test results and qualitative indicator rating data to generate a hybrid evaluation dataset; Calculate the membership values ​​of the mixed evaluation dataset for different comment intervals within a pre-defined set of comments, and combine the membership values ​​to generate a membership array; Retrieve the fixed scores corresponding to different comment intervals within the preset comment set, and extract the membership array; Perform matrix multiplication on the membership array and the fixed score to generate and output equipment data verification information.

[0010] Furthermore, the geological disaster life detection equipment performance evaluation and demonstration system of the present invention has multiple underlying sensors, including: Soil temperature and humidity sensors are installed around the target test site to monitor the fluctuation data of moisture content inside the target test site and convert the moisture content fluctuation data into raw sensing messages. The high-pressure sensor is deployed in the unstable area of ​​the target disaster body to collect information on stress changes inside the target disaster body and integrate the stress change information into the original sensing message; Ultra-wideband positioning base stations receive radio frequency signals fed back by target positioning tags. The target positioning tags have unique identification codes. The radio frequency signals are converted into a position coordinate stream, and the position coordinate stream is converted into a set of three-dimensional coordinate points.

[0011] Furthermore, the geological disaster life detection equipment performance evaluation and display system of the present invention includes an underlying data resource pool, which comprises four independent storage partitions: The basic data area stores data dictionary text, organizational lists, and system user information. The business data area calls upon the contents of the basic data area to store preset evaluation index parameters, qualitative index rating data, and equipment comprehensive performance scores. The interface data area receives the raw sensing messages transmitted by the sensing and acquisition module and the docking messages generated by the ultra-wideband positioning base station and caches them locally. The audit log area records sensor monitoring logs generated from raw sensing messages and manual evaluation behavior logs generated from qualitative indicator rating data transmission.

[0012] Furthermore, the geological disaster life detection equipment performance evaluation and demonstration system of the present invention includes a standard interface adapter unit in the sensing and acquisition module, which comprises: Analog driver firmware is used to connect to an external water pressure sensor and acquire dual-mode acquisition data output by the water pressure sensor. The signal conditioning driver firmware receives the millivolt-level differential signal generated by the heavy pressure sensor and performs analog-to-digital conversion on the millivolt-level differential signal to generate digital output data. The bus parsing driver firmware reads the single-bus communication protocol messages from the soil temperature and humidity sensor, parses the single-bus communication protocol messages to generate standardized general objects, and combines the dual-mode acquired data, digital output data, and standardized general objects to generate the original sensing message.

[0013] Furthermore, in the geological disaster life detection equipment performance evaluation and demonstration system of the present invention, the performance evaluation module is configured to operate according to the business management architecture, which is configured with three control mechanisms: The weight allocation mechanism calls a preset dynamic weight allocation algorithm to process the aggregated data stream transmitted by the sensing and acquisition module and generate adaptive quantitative scoring results. The collaborative evaluation mechanism triggers preset multi-level approval rules based on adaptive quantitative scoring results, binds specific role electronic signatures at the verification nodes of the multi-level approval rules, and generates review record documents including electronic signatures. The instrument calibration mechanism extracts sensor status characteristics based on the aggregated data stream, compares the sensor status characteristics with preset standard values, generates a periodic sensor calibration command when the deviation between the sensor status characteristics and the preset standard values ​​exceeds a preset range, collects calibration feedback data, verifies the calibration feedback data, and outputs an instrument availability status indicator.

[0014] Furthermore, in the geological disaster life detection equipment performance evaluation and demonstration system described in this invention, the sensing and acquisition module processes the original sensing messages through a two-level collaborative parsing mechanism. The two-level collaborative parsing mechanism is as follows: The protocol parsing engine deployed at the system edge receives the raw sensing messages, performs frame synchronization processing, cyclic redundancy check, and byte order alignment conversion on the raw sensing messages, and generates parsed data. Append an absolute timestamp to the parsed data to generate a standard object sequence that includes the absolute timestamp; The streaming data receiving service node deployed in the system cloud receives the standard object sequence transmitted from the system edge side, and uses a pre-built abnormal data detection algorithm to perform abnormal value filtering calculations on the standard object sequence to generate time series data; The time series data is inserted in batches and written to the distributed relational database connected to the communication connection. The time series data is then read from the distributed relational database and aggregated calculations are performed to generate an aggregated data stream.

[0015] Furthermore, in the geological disaster life detection equipment performance evaluation and display system described in this invention, the sensing and acquisition module is connected to a remote terminal maintenance container. The remote terminal maintenance container automatically executes peripheral device long connection pool management logic, disconnection reconnection logic, periodic connection status keep-alive message detection logic, communication link quality prediction logic, and terminal embedded firmware differential upgrade logic.

[0016] Beneficial effects of this invention; This invention, relying on standardized hardware and software interfaces and a distributed service architecture, automates the entire process of evaluation parameter input, multi-source sensor information access, on-site testing environment monitoring, and comprehensive display of test results. It solves the technical problems of cumbersome data processing, high error rates in calculations, and the difficulty in uniformly storing, querying, and securely archiving massive amounts of surveying data in traditional manual evaluations. The system incorporates a multi-level fuzzy comprehensive evaluation engine, which deeply integrates expert qualitative indicator rating data with quantitative indicator test results collected on-site through hierarchical analysis and entropy weight calculation. This effectively eliminates subjective biases caused by single evaluation methods, making the final output equipment comprehensive performance score more scientific, accurate, and objective.

[0017] The system renders a 3D electronic map on the terminal screen and dynamically overlays the equipment's overall performance score onto the corresponding coordinate layer, providing an intuitive and user-friendly interface and lowering the operational threshold for professional evaluation software. Simultaneously, an audit log area is deployed at the underlying level, configured with multi-level approval and electronic signature mechanisms, ensuring the explainability and legal compliance traceability of the review process. Attached Figure Description

[0018] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0019] Figure 1 Overall architecture diagram of the geological disaster life detection equipment performance evaluation and demonstration system provided in this embodiment of the invention; Figure 2 The system structure diagram of the pre-set performance evaluation index of the geological disaster life detection equipment provided in the embodiments of the present invention is shown. Detailed Implementation

[0020] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0021] Please see Figure 1 as well as Figure 2 The present invention provides a system for evaluating and demonstrating the effectiveness of geological disaster life detection equipment, comprising: The underlying sensors are deployed at the test site to collect the environmental conditions and send out raw sensing messages. Target positioning tags are configured on the personnel and equipment being inspected to provide basic location data; The display terminal and communication connection environment display module are used to provide a visual display interface. The sensing and acquisition module connects to the underlying sensors, receives the raw sensing messages sent by the underlying sensors, combines the raw sensing messages to generate an aggregated data stream, and transmits the aggregated data stream to the performance evaluation module. The positioning and tracking module acquires the set of three-dimensional coordinate points corresponding to the target positioning tag, parses the three-dimensional coordinate point set to generate the location information of the personnel being detected and the location information of the equipment being detected, and transmits the location information of the personnel being detected and the location information of the equipment being detected to the environment display module. The performance evaluation module receives the aggregated data stream transmitted by the sensing and acquisition module, reads the preset evaluation index parameters, performs hierarchical analysis calculations based on the aggregated data stream and the preset evaluation index parameters, generates the equipment comprehensive performance score, and transmits the equipment comprehensive performance score to the environment display module. The environment display module receives the location information of the personnel being tested and the location information of the equipment being tested transmitted by the positioning and tracking module, extracts the preset 3D electronic map and renders the preset 3D electronic map on the display terminal, obtains the comprehensive equipment performance score transmitted by the performance evaluation module, and overlays and displays the comprehensive equipment performance score on the coordinate layer corresponding to the location information of the equipment being tested within the preset 3D electronic map.

[0022] In a specific implementation scenario, please refer to Figure 1 as well as Figure 2 A geological disaster life detection equipment performance evaluation and demonstration system was deployed at a debris flow simulation test site. Temperature and humidity sensors and stress sensors were installed around the debris flow simulation test site. These sensors collected data on the environmental conditions and emitted raw sensing messages. During the test, the rescue personnel and life detection equipment were equipped with uniquely identified target positioning tags to provide basic location data. Once the system was operational, the sensing and acquisition module connected to the temperature and humidity sensors and stress sensors, received the raw sensing messages, combined them to generate an aggregated data stream, and transmitted the aggregated data stream to the performance evaluation module. Simultaneously, the positioning and tracking module acquired the set of three-dimensional coordinate points corresponding to the target positioning tags, parsed the set to generate the location information of the personnel and equipment being detected, and sent this information to the environmental demonstration module. After acquiring the data, the performance evaluation module read preset evaluation index parameters, performed hierarchical analysis calculations based on the aggregated data stream, derived a comprehensive equipment performance score, and transmitted this score to the environmental demonstration module. Finally, the environment display module extracts a preset 3D electronic map and renders it on the display terminal with the established communication connection. The environment display module calls the location information of the detected equipment and the equipment's comprehensive performance score, and dynamically overlays and displays the equipment's comprehensive performance score on the coordinate layer corresponding to the location information of the detected equipment within the preset 3D electronic map, providing an intuitive visual display interface.

[0023] This invention provides a system for evaluating and displaying the effectiveness of geological disaster life detection equipment. The system is built upon standardized hardware and software interfaces and a distributed service architecture; it automates the entire process of inputting evaluation parameters, accessing multi-source sensor information, monitoring the on-site testing environment, and comprehensively displaying test results, thus solving the technical problems of cumbersome calculations, error-proneness, and difficulty in unified storage and retrieval associated with traditional manual evaluation.

[0024] The system composition and logical architecture mainly include a perception and acquisition module, a positioning and tracking module, a performance evaluation module, and an environment display module.

[0025] The sensing and acquisition module receives data from the test site. It connects to environmental monitoring sensors and equipment status sensors, and receives raw sensing messages from these sensors. To mask differences in heterogeneous communication protocols, the sensing and acquisition module uses a unified data format at the logic layer of its program to combine the raw sensing messages into an aggregated data stream. This aggregated data stream is then transmitted to the performance evaluation module via a secure transmission channel (e.g., a TLS-encrypted communication channel).

[0026] The spatial location acquisition and parsing process is performed by the positioning and tracking module. This module acquires the set of 3D coordinate points corresponding to the target positioning tag. In specific implementation scenarios, both the personnel being inspected (or the dummy system) and the equipment being inspected are equipped with ultra-wideband positioning tags with unique identification codes. The positioning and tracking module receives the coordinate stream sent from the ultra-wideband network, parses the 3D coordinate point set to generate the location information of the personnel and the equipment being inspected, and simultaneously transmits this information to the environment display module.

[0027] The performance evaluation module serves as the core computing node of the system. It receives aggregated data streams from the sensing and acquisition module and simultaneously reads preset evaluation index parameters from the system storage area. Based on the aggregated data streams, including real-time test data, and the preset evaluation index parameters, it performs built-in hierarchical analysis calculations and data processing; generates an objective and quantifiable overall equipment performance score, and transmits this score to the environment display module.

[0028] The visual human-computer interaction interface is presented through the environment display module. This module receives the location information of the personnel and equipment being tested from the positioning and tracking module; it extracts a pre-built 3D electronic map and renders it on the connected display terminal. Subsequently, the environment display module obtains the overall equipment performance score from the performance evaluation module and overlays and displays this score in real-time on the coordinate layer corresponding to the equipment's location information on the pre-built 3D electronic map, thus achieving a fusion presentation of the test scenario and performance data.

[0029] The performance evaluation module is further divided into an expert management sub-module, an indicator management sub-module, a matching evaluation sub-module, a manual evaluation sub-module, and a report output sub-module, in order to achieve a deep integration of hierarchical analysis and fuzzy comprehensive evaluation.

[0030] The expert management submodule establishes and maintains expert information including name, major, and affiliated institution; it collects the identity information of experts participating in the scoring and qualitative indicator rating data, and sends the qualitative indicator rating data to the manual evaluation submodule to ensure that the evaluation results have verifiability and compliance record attributes.

[0031] The pre-set performance evaluation index system is stored in the index management submodule. Based on the objective characteristics of geological disaster life detection equipment, the pre-set performance evaluation index system is divided into a target layer, a criterion layer covering basic parameters, detection capabilities, positioning capabilities, and environmental adaptability, and an index layer covering weight, size, endurance, detection depth, and water content range. The index management submodule receives modification instructions that affect the pre-set performance evaluation index system, dynamically adjusts nodes according to the modification instructions, and outputs the preset evaluation index parameters to the matching evaluation submodule.

[0032] The matching evaluation submodule performs hierarchical analysis and entropy weight combination calculation. The specific calculation logic includes: extracting preset evaluation index parameters and constructing a multi-level structured judgment matrix; performing matrix eigenvalue calculation to generate initial weights corresponding to the elements of the multi-level structured judgment matrix; calculating the consistency ratio of the multi-level structured judgment matrix based on the initial weights and the largest eigenvalue of the multi-level structured judgment matrix; determining whether the consistency ratio is less than a preset consistency check threshold; if the consistency ratio is less than the preset consistency check threshold, invoking the entropy weight algorithm formula to process the multi-level structured judgment matrix and the initial weights to generate comprehensive index weight values; constructing a comprehensive weight matrix from the comprehensive index weight values ​​and sending it to the report output submodule.

[0033] The manual evaluation submodule performs integrated processing on qualitative and quantitative indicators. The program architecture of the manual evaluation submodule integrates a multi-level fuzzy comprehensive evaluation engine, and its execution logic is as follows: It receives qualitative indicator rating data sent by the expert management submodule and obtains quantitative indicator test results entered through the input interface; it integrates the quantitative indicator test results and qualitative indicator rating data to generate a hybrid evaluation dataset; it calculates the membership degree values ​​of the hybrid evaluation dataset for different comment intervals within a preset comment set, and combines the membership degree values ​​to generate a membership degree array; it retrieves the fixed scores corresponding to different comment intervals included in the preset comment set and extracts the membership degree array; it performs matrix multiplication operations on the membership degree array and the fixed scores to generate and output equipment data verification information.

[0034] The step of generating the final document is performed by the report output submodule. The report output submodule receives the comprehensive weight matrix sent by the matching evaluation submodule and the equipment data verification information sent by the manual evaluation submodule; it performs matrix multiplication calculations based on the comprehensive weight matrix and the equipment data verification information to generate the final comprehensive performance document, thereby completing the evaluation logic loop.

[0035] In the hardware monitoring network deployment structure, the sensing and acquisition module connects to multiple sensors via communication cables and a network bus. The connected sensors specifically include: a soil temperature and humidity sensor, installed around the test site, which monitors moisture content fluctuations within the test site area and converts this data into raw sensing messages; a pressure sensor, deployed at weak points in the disaster-prone area, which collects stress change information within the disaster structure and integrates this stress change information into the raw sensing messages to ensure the safety of the test environment; and an ultra-wideband positioning base station, which receives radio frequency signals from target positioning tags with unique identifiers, converts these signals into a location coordinate stream, and then converts the location coordinate stream into a set of three-dimensional coordinate points.

[0036] The standard interface adapter unit is configured in the sensing and acquisition module to perform standardized protocol adaptation operations for multi-source heterogeneous sensors. The standard interface adapter unit includes: analog signal driver firmware, which connects to the analog transmission channel of the water pressure sensor to acquire dual-mode acquisition data; signal conditioning driver firmware, which receives the millivolt-level differential signal from the heavy pressure sensor and performs analog-to-digital conversion on the millivolt-level differential signal to generate digital output data; and bus parsing driver firmware, which reads the communication protocol messages of the soil temperature and humidity sensor's serial bus, parses the communication protocol messages to generate standardized universal objects; the standard interface adapter unit ultimately combines the dual-mode acquisition data, digital output data, and standardized universal objects to generate a unified raw sensing message.

[0037] During the operation of the standard interface adapter unit, the bus parsing driver firmware reads the single-bus communication protocol messages from the soil temperature and humidity sensor. The single-bus communication protocol refers to providing power supply and bidirectional data transmission simultaneously through a single signal transmission cable. After the bus parsing driver firmware issues a reset signal, it reads a string of binary data within the single-bus communication protocol message and converts it into a standardized universal object including moisture content fluctuation data. Simultaneously, the analog driver firmware acquires dual-mode acquisition data from the water pressure sensor output. Specifically, dual-mode acquisition data refers to voltage acquisition mode and current acquisition mode. The analog driver firmware internally integrates a voltage conditioning switch and a current load resistor, enabling it to automatically switch between 0-5V voltage signal acquisition and 4-20mA current signal acquisition modes depending on the type of connected water pressure sensor. The signals acquired in both modes are collectively referred to as dual-mode acquisition data.

[0038] The raw sensing messages are processed by the sensing acquisition module through a two-level collaborative parsing mechanism. In the edge-side processing stage, the protocol parsing engine deployed on the edge device receives the raw sensing messages; it performs frame synchronization processing, cyclic redundancy check, and byte order alignment conversion on the raw sensing messages to generate parsed data; it appends an absolute timestamp to the parsed data, generating a standard object sequence including the absolute timestamp. In the cloud-side processing stage, the streaming data receiving service node receives the standard object sequence transmitted from the edge side; it uses an anomaly detection algorithm to perform anomaly value filtering calculations on the standard object sequence, generating valid time-series data; it executes batch insertion instructions on the valid time-series data and writes it into a distributed relational database; it reads the valid time-series data from the distributed relational database, performs aggregation calculations, generates an aggregated data stream, and transmits it to the performance evaluation module.

[0039] The business management architecture supports the operation of the performance evaluation module and is configured with three core control mechanisms. These mechanisms include: a weight allocation mechanism, which uses a dynamic weight allocation algorithm to process the aggregated data stream transmitted by the sensing and acquisition module, generating adaptive quantitative scoring results; a collaborative evaluation mechanism, which triggers multi-level approval rules based on the adaptive quantitative scoring results, binds specific role electronic signatures at the verification nodes of the multi-level approval rules, and generates review record documents including electronic signatures; and an instrument calibration mechanism, which extracts sensor status characteristics from the aggregated data stream and compares them with preset standard values; when the comparison deviation exceeds a preset range, it generates periodic sensor calibration instructions, collects calibration feedback data, and verifies it to output an instrument availability status indicator.

[0040] The instrument calibration mechanism is internally configured with a reference voltage value and an allowable voltage fluctuation range. The reference voltage value is set to 5 volts, and the allowable voltage fluctuation range is set to a fluctuation range of 5% above and below the reference voltage value. The instrument calibration mechanism extracts the current operating voltage from the sensor's status feature feedback, compares the current operating voltage with the reference voltage value, and determines whether the current operating voltage falls within the allowable voltage fluctuation range. If the current operating voltage exceeds the allowable voltage fluctuation range, the instrument calibration mechanism generates a periodic calibration command for the sensor.

[0041] The performance evaluation module retrieves a preset dynamic weight allocation algorithm. The execution steps of the dynamic weight allocation algorithm include: first, receiving the aggregated data stream and calculating the variance values ​​of each monitoring indicator within the current sliding time window; second, calling the initial comprehensive weight in the comprehensive weight matrix; and finally, multiplying the initial comprehensive weight with the weighted term of the variance value and performing normalization processing to generate a quantitative scoring result that adaptively fluctuates with the on-site data.

[0042] The formula for calculating the adaptive quantization score result using the dynamic weight allocation algorithm is as follows: in, Represents the first in the aggregated data stream The dynamic weights upon which the adaptive quantitative scoring results of each monitoring indicator are based; The representation is the one called from the comprehensive weight matrix. The initial comprehensive weights corresponding to each monitoring indicator; Represents the calculation of the current sliding time window. The variance values ​​obtained from the monitoring indicators; This represents the total number of monitoring indicators included in the aggregated data stream; The representation is the one called from the comprehensive weight matrix. The initial comprehensive weights corresponding to each monitoring indicator; Represents the calculation of the current sliding time window. The variance values ​​obtained from the monitoring indicators; The sequence number representing the monitoring indicator; The sequence number represents the monitoring indicator.

[0043] Furthermore, the cloud-based streaming data receiving service node employs a pre-built anomaly detection algorithm, specifically using a rolling time window mean-variance test model. The streaming data receiving service node truncates a data window of fixed length from the input standard object sequence, calculates the statistical mean and standard deviation of historical data within the data window, and if the absolute value of the difference between a newly entered value and the statistical mean exceeds three times the standard deviation, the newly entered value is determined to be an anomaly caused by sudden impulse interference and is directly filtered out, thereby generating time-series data.

[0044] Formula for calculating the historical data average of anomaly detection algorithms: in, This represents the statistical mean of historical data captured within the data window. This represents the total number of standard object sequence samples included within the captured data window; Represents the data window containing the first data segment arranged in time series. Historical data; The sorting number represents the historical data.

[0045] Formula for calculating the standard deviation of historical data in anomaly detection algorithms: in, This represents the standard deviation of historical data captured within the data window. This represents the total number of standard object sequence samples included within the captured data window; Represents the data window containing the first data segment arranged in time series. Historical data; This represents the statistical mean of historical data captured within the data window. The sorting number represents the historical data.

[0046] In application scenarios involving geological hazards such as debris flows and landslides, environmental factors and equipment performance parameters exhibit a nonlinear mapping relationship. To establish the connection between the application scenario and the system algorithm's data processing logic, the anomaly data detection algorithm and the multi-level fuzzy comprehensive evaluation engine are configured with input-output data mapping specifications. The input data for the anomaly data detection algorithm is set as a standard object sequence, which includes water content fluctuation data and stress change information. Based on the objective change cycle of soil moisture infiltration rate and stress release within the rock and soil structure during the debris flow evolution stage, the time window length within the anomaly data detection algorithm's program layer is set to 60 seconds, and the sampling frequency is set to 10 Hz.

[0047] After reading 600 sampling points within the time window, the anomaly detection algorithm extracts the sampling point values ​​to form an input data matrix. Under conditions where strong electromagnetic interference from the external geological environment causes sudden voltage changes in the external probe, the absolute value of the differences in some values ​​in the input data matrix exceeds three times the standard deviation. The anomaly detection algorithm performs numerical filtering calculations and outputs time-series data after filtering out the sudden voltage changes, thereby eliminating the interference of electromagnetic noise from the complex geological environment on the soil temperature and humidity sensor and the pressure sensor.

[0048] Following the processing flow of the anomaly detection algorithm, the input data of the multi-level fuzzy comprehensive evaluation engine is set as a hybrid evaluation dataset. This dataset is formed by concatenating quantitative indicator test results and qualitative indicator rating data, and constitutes a one-dimensional tensor within the computational unit. The multi-level fuzzy comprehensive evaluation engine's computational architecture is internally configured with a membership transformation function. This function extracts the objective limit values ​​of the detection depth required for geological disaster life detection equipment in actual search and rescue operations, as well as the protection level assessment requirements. After obtaining the objective limit values ​​and assessment requirements, the membership transformation function maps the discrete values ​​within the one-dimensional tensor data structure to a continuous real number interval between 0 and 1, completing the mapping calculation operation and generating membership values.

[0049] After the membership degree numerical generation step, the output data of the multi-level fuzzy comprehensive evaluation engine is set as a one-dimensional numerical result, which consists of equipment data verification information. This equipment data verification information directly characterizes the comprehensive search and rescue effectiveness of geological disaster life detection equipment under specific moisture content and stress environments. The overall setting of input and output data clearly establishes the intrinsic correlation between the original sensing messages, quantitative indicator test results, and equipment data verification information.

[0050] The multi-level fuzzy comprehensive evaluation engine uses a Gaussian distribution function to construct a membership transformation function model; system designers set fixed expectation and variance parameters for the Gaussian distribution function. During the data input phase, the multi-level fuzzy comprehensive evaluation engine receives quantitative indicator test results and qualitative indicator rating data, and uses these results as input parameters for the membership transformation function model.

[0051] After entering the computation stage, the membership transformation function model extracts the input parameters and performs probability calculation by substituting the input parameters into a Gaussian distribution function, thereby generating continuous probability values ​​in the range of 0 to 1. Subsequently, the multi-level fuzzy comprehensive evaluation engine maps the continuous probability values ​​to a preset evaluation interval, and finally outputs the corresponding membership values.

[0052] In the streaming data processing workflow, the cloud-based streaming data receiving service node constructs a fixed-length first-in-first-out (FIFO) data queue and establishes a rolling time window mean-variance test model. The streaming data receiving service node pushes time-series data into the FIFO data queue sequentially. Each time new time-series data is received and pushed into the queue, the rolling time window mean-variance test model simultaneously removes the oldest time-series data from the front of the FIFO data queue. Finally, the rolling time window mean-variance test model extracts all values ​​from the updated FIFO data queue storage space and performs numerical calculations of the arithmetic mean and standard deviation.

[0053] Anomaly detection algorithm, anomaly value filtering calculation and judgment formula: in, This represents the absolute value of the difference between the newly entered value and the statistical mean. This represents the newly entered value that the system has received from the standard object sequence; This represents the statistical mean of historical data captured within the data window. This represents the standard deviation of historical data captured within the data window.

[0054] The system's underlying configuration includes a data resource pool with four independent storage partitions. The specific distribution is as follows: The basic data area stores data dictionary text, organizational lists, and system user information; the business data area accesses the content stored in the basic data area, storing preset evaluation index parameters, qualitative index rating data, and equipment comprehensive performance scores; the interface data area receives raw sensing messages transmitted by the sensing and acquisition module and docking messages generated by the ultra-wideband positioning base station, while simultaneously using a high-speed caching component to perform local caching operations; and the audit log area records sensor monitoring logs generated from raw sensing messages and manual evaluation behavior logs generated from qualitative index rating data transmission.

[0055] Maintaining the continuous availability of on-site monitoring terminals relies on remote terminal maintenance containers. The sensing and acquisition module connects to the remote terminal maintenance container. Through a high-concurrency communication framework, the remote terminal maintenance container automatically executes peripheral device long connection pool management logic, disconnection reconnection logic, periodic connection status keep-alive message detection logic, communication link quality prediction logic, and terminal embedded firmware differential upgrade logic, completing automated operation and maintenance throughout the entire lifecycle of the hardware device.

[0056] When managing field observation terminals, the remote terminal operation and maintenance container automatically executes five control logics, the specific mechanisms of which are as follows: First, the peripheral device long connection pool management logic maintains multiple network communication sockets for multiple field observation terminals within the cache area of ​​the system's basic data resource pool, eliminating the waiting time for frequent connection creation; Second, the disconnection reconnection logic automatically starts a 5-second periodic polling attempt to re-initiate a connection to the observation terminal when a communication link interruption is detected; Third, the periodic connection status keep-alive message detection logic determines the liveness status of the observation terminal by receiving a fixed-byte verification code sent by the observation terminal every 30 seconds; Fourth, the communication link quality estimation logic calculates a quantitative value of link quality by statistically analyzing the packet loss rate and latency of the past 100 data packets; Fifth, the terminal embedded firmware differential upgrade logic, during version updates, only sends a difference patch package of the old and new firmware binary files from the cloud, and the central processing unit of the field observation terminal writes the difference patch package to a specified sector of the flash memory space inside the observation terminal to complete the differential upgrade.

[0057] The indicator management submodule stores a pre-set performance evaluation indicator system. This system is divided into a target layer, a criterion layer, and an indicator layer. The target layer is set as the overall effectiveness of the geological disaster life detection equipment. The criterion layer includes basic parameter nodes, detection capability nodes, positioning capability nodes, and environmental adaptability nodes. The indicator layer includes evaluation nodes such as weight, dimensions, operating time, detection depth, and water content range.

[0058] The matching evaluation submodule extracts preset evaluation index parameters and constructs a multi-level structural judgment matrix. The multi-level structural judgment matrix includes a comparison matrix between the target layer and the criterion layer, and a comparison matrix between the criterion layer and the index layer.

[0059] Table 1. Example of a multi-level structure judgment matrix comparing the target layer and the criterion layer. The matching and evaluation submodule performs matrix eigenvalue calculations according to matrix operation rules. The program generates initial weights for each indicator within the multi-level structured judgment matrix. Subsequently, the matching and evaluation submodule calculates the consistency ratio of the multi-level structured judgment matrix based on the initial weights and the largest eigenvalue of the matrix. The system then determines whether the consistency ratio is less than a preset consistency check threshold.

[0060] The matching and evaluation submodule has a built-in benchmark value for matrix consistency judgment. It sets a preset consistency check threshold of 0.1, extracts the calculated consistency ratio value, and compares it to 0.1. If the consistency ratio value is less than 0.1, the matching and evaluation submodule determines that the multi-level structure judgment matrix passes the consistency check step. If the consistency ratio value is greater than or equal to 0.1, the matching and evaluation submodule rejects the multi-level structure judgment matrix and outputs a prompt message guiding the user to re-enter the indicator evaluation data.

[0061] Formula for calculating the consistency index of multi-level structure judgment matrix: in, A consistency index representing a multi-level structure judgment matrix; The largest eigenvalue representing the multi-level structure judgment matrix; This represents the order of the indicators included in the multi-level structure judgment matrix.

[0062] Formula for calculating the consistency ratio of the multi-level structure judgment matrix: in, The value representing the consistency ratio of the multi-level structure judgment matrix; A consistency index representing a multi-level structure judgment matrix; The average random consistency index represents the judgment matrix of a multi-level structure.

[0063] Table 2 Initial weights and consistency test calculation results When the consistency ratio is less than the preset consistency check threshold, the matching evaluation submodule retrieves the entropy weight algorithm formula. The processing unit uses the entropy weight algorithm to process the multi-level structure judgment matrix and the initial weights, generating a comprehensive index weight value. The matching evaluation submodule then constructs a comprehensive weight matrix from the comprehensive index weight values.

[0064] The formula for calculating information entropy using the entropy weighting algorithm is as follows: in, The first element in the multi-level structure judgment matrix represents the first element. Information entropy of the indicator; The total number of evaluation objects included in the hybrid evaluation dataset generated by the human evaluation submodule that participated in the evaluation; Representing the The assessment subject was in the first The characteristic proportions calculated under the preset evaluation index parameters; Represents the natural logarithm operator; The sequence number representing the evaluation object; This represents the sorting number of the preset evaluation index parameters.

[0065] Entropy weight algorithm comprehensive index weight calculation formula: in, The first element in the multi-level structure judgment matrix represents the first element. The weight value of the comprehensive indicator generated from the individual indicators; The first element in the multi-level structure judgment matrix represents the first element. Information entropy of the indicator; This represents the total number of preset evaluation index parameters within the multi-level structure judgment matrix. This represents the sorting number of the preset evaluation index parameters.

[0066] Table 3 Example of Comprehensive Weight Matrix The manual evaluation submodule receives the quantitative indicator test results entered through the input interface. Table 4 shows the evaluation criteria for the quantitative indicator test results stored in the system. The evaluation criteria are divided into three preset comment intervals.

[0067] Table 4 Evaluation Criteria for Quantitative Index Test Results The expert management submodule collects the identity information of the experts participating in the scoring, as well as the qualitative indicator rating data. The expert management submodule then sends the qualitative indicator rating data to the manual evaluation submodule.

[0068] Table 5. Qualitative Indicator Rating Data Pre-set Comment Set Standards During the evaluation process, the manual evaluation submodule obtains the actual numerical values ​​output by the testing process. Table 6 shows the specific quantitative indicator test results entered. Table 7 shows the qualitative indicator rating data entered by experts through the terminal.

[0069] Table 6 Quantitative Index Test Results Table 7 Example of Qualitative Indicator Rating Data Entry The manual evaluation submodule integrates quantitative indicator test results and qualitative indicator rating data. The integration program combines the quantitative indicator test results and qualitative indicator rating data to generate a hybrid evaluation dataset. The manual evaluation submodule calculates the membership values ​​of the hybrid evaluation dataset for different comment intervals within a preset comment set. The numerical processing unit combines all membership values ​​to generate a membership array.

[0070] Table 8 Calculation results of the target layer membership array The manual evaluation submodule retrieves fixed scores corresponding to different score intervals within a preset set of comments. The execution program extracts the membership degree array and performs matrix multiplication between the membership degree array and the fixed scores. The matrix multiplication operation generates and outputs the final equipment data verification information, completing the multi-level fuzzy comprehensive evaluation process.

[0071] Multi-level fuzzy comprehensive evaluation engine matrix multiplication operation formula: in, This represents the equipment data verification information output by the manual evaluation submodule; This represents the membership array generated by combining the membership values ​​obtained from the computation of the mixed evaluation dataset; This represents a column vector constructed by retrieving fixed scores corresponding to different comment intervals within a preset comment set.

[0072] The multi-level fuzzy comprehensive evaluation engine is configured with fixed scores corresponding to a preset set of comments. In a specific implementation scenario, the preset set of comments includes a first comment interval, a second comment interval, and a third comment interval. The fixed scores corresponding to the three comment intervals are set to 60, 80, and 100 points respectively. The fixed score column vector constructed based on the fixed scores is as follows: The matrix multiplication operation steps are explained in detail based on the calculation results in Table 8: After integrating multiple data points, the manual evaluation submodule calculates the membership degree array of the geological disaster life detection equipment for the three evaluation intervals. A multi-level fuzzy comprehensive evaluation engine extracts the membership degree array. and the membership array With fixed score column vector Perform matrix multiplication. The specific calculation process is as follows: Overall score = The score of 86.324, calculated by the multi-level fuzzy comprehensive evaluation engine, is the equipment data verification information output by the manual evaluation submodule. The manual evaluation submodule sends the equipment data verification information to the report output submodule. The report output submodule receives the equipment data verification information and the comprehensive weight matrix, performs matrix multiplication on the equipment data verification information and the comprehensive weight matrix, and generates the final comprehensive performance document.

Claims

1. A system for evaluating and demonstrating the effectiveness of geological disaster life detection equipment, characterized in that, include: The underlying sensors are deployed at the test site to collect the environmental conditions and send out raw sensing messages. Target positioning tags are configured on the personnel and equipment being inspected to provide basic location data; The display terminal and communication connection environment display module are used to provide a visual display interface. The sensing and acquisition module connects to the underlying sensors, receives the raw sensing messages sent by the underlying sensors, combines the raw sensing messages to generate an aggregated data stream, and transmits the aggregated data stream to the performance evaluation module. The positioning and tracking module acquires the set of three-dimensional coordinate points corresponding to the target positioning tag, parses the three-dimensional coordinate point set to generate the location information of the personnel being detected and the location information of the equipment being detected, and transmits the location information of the personnel being detected and the location information of the equipment being detected to the environment display module. The performance evaluation module receives the aggregated data stream transmitted by the sensing and acquisition module, reads the preset evaluation index parameters, performs hierarchical analysis calculations based on the aggregated data stream and the preset evaluation index parameters, generates the equipment comprehensive performance score, and transmits the equipment comprehensive performance score to the environment display module. The environment display module receives the location information of the personnel being tested and the location information of the equipment being tested transmitted by the positioning and tracking module, extracts the preset 3D electronic map and renders the preset 3D electronic map on the display terminal, obtains the comprehensive equipment performance score transmitted by the performance evaluation module, and overlays and displays the comprehensive equipment performance score on the coordinate layer corresponding to the location information of the equipment being tested within the preset 3D electronic map.

2. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 1, characterized in that, The performance evaluation module includes: The expert management submodule collects the identity information of the experts participating in the scoring and the qualitative indicator rating data, and sends the qualitative indicator rating data to the manual evaluation submodule. The indicator management submodule stores the preset performance evaluation indicator system, receives modification instructions that apply to the preset performance evaluation indicator system, and outputs preset evaluation indicator parameters to the matching evaluation submodule according to the modification instructions. The matching evaluation submodule receives the preset evaluation index parameters output by the index management submodule, performs hierarchical analysis and entropy weight calculation based on the preset evaluation index parameters, generates a comprehensive weight matrix, and sends the comprehensive weight matrix to the report output submodule. The manual evaluation submodule is equipped with an input interface. It receives qualitative indicator rating data sent by the expert management submodule, obtains quantitative indicator test results entered through the input interface, integrates the qualitative indicator rating data and the quantitative indicator test results to generate equipment data verification information, and sends the equipment data verification information to the report output submodule. The report output submodule receives the comprehensive weight matrix sent by the matching evaluation submodule and the equipment data verification information sent by the manual evaluation submodule. Based on the comprehensive weight matrix and the equipment data verification information, it performs matrix multiplication calculations to generate the final comprehensive performance document.

3. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 2, characterized in that, The matching evaluation submodule is configured as follows: Extract the preset evaluation index parameters and construct a multi-level structured judgment matrix based on the preset evaluation index parameters; Perform matrix eigenvalue calculation to generate the initial weights for each index within a multi-level structured judgment matrix; Based on the initial weights and the largest eigenvalue of the multi-level structure judgment matrix, calculate the consistency ratio of the multi-level structure judgment matrix and determine whether the consistency ratio is less than the preset consistency test threshold. When the consistency ratio is less than the preset consistency test threshold, the preset entropy weight algorithm formula is retrieved to process the multi-level structure judgment matrix and the initial weights, generate the comprehensive index weight values, and construct the comprehensive index weight matrix.

4. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 3, characterized in that, The manual evaluation submodule integrates a multi-level fuzzy comprehensive evaluation engine, which is configured as follows: Integrate quantitative indicator test results and qualitative indicator rating data to generate a hybrid evaluation dataset; Calculate the membership values ​​of the mixed evaluation dataset for different comment intervals within a pre-defined set of comments, and combine the membership values ​​to generate a membership array; Retrieve the fixed scores corresponding to different comment intervals within the preset comment set, and extract the membership array; Perform matrix multiplication on the membership array and the fixed score to generate and output equipment data verification information.

5. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 4, characterized in that, The underlying sensors are numerous and include: Soil temperature and humidity sensors are installed around the target test site to monitor the fluctuation data of moisture content inside the target test site and convert the moisture content fluctuation data into raw sensing messages. The high-pressure sensor is deployed in the unstable area of ​​the target disaster body to collect information on stress changes inside the target disaster body and integrate the stress change information into the original sensing message; The ultra-wideband positioning base station receives radio frequency signals fed back by the target positioning tag. The target positioning tag has a unique identification code. The radio frequency signal is converted into a position coordinate stream, and the position coordinate stream is converted into a three-dimensional coordinate point set.

6. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 5, characterized in that, The geological disaster life detection equipment effectiveness assessment and demonstration system has an underlying data resource pool, which includes four independent storage partitions: The basic data area stores data dictionary text, organizational lists, and system user information. The business data area calls upon the contents of the basic data area to store preset evaluation index parameters, qualitative index rating data, and equipment comprehensive performance scores. The interface data area receives the raw sensing messages transmitted by the sensing and acquisition module and the docking messages generated by the ultra-wideband positioning base station and caches them locally. The audit log area records sensor monitoring logs generated from raw sensing messages and manual evaluation behavior logs generated from qualitative indicator rating data transmission.

7. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 6, characterized in that, The sensing and acquisition module is equipped with a standard interface adapter unit, which includes: Analog driver firmware is used to connect to an external water pressure sensor and acquire dual-mode acquisition data output by the water pressure sensor. The signal conditioning driver firmware receives the millivolt-level differential signal generated by the heavy pressure sensor and performs analog-to-digital conversion on the millivolt-level differential signal to generate digital output data. The bus parsing driver firmware reads the single-bus communication protocol messages from the soil temperature and humidity sensor, parses the single-bus communication protocol messages to generate standardized general objects, and combines the dual-mode acquired data, digital output data, and standardized general objects to generate the original sensing message.

8. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 7, characterized in that, The performance evaluation module is configured to operate according to the business management architecture, which has three control mechanisms: The weight allocation mechanism calls a preset dynamic weight allocation algorithm to process the aggregated data stream transmitted by the sensing and acquisition module and generate adaptive quantitative scoring results. The collaborative evaluation mechanism triggers preset multi-level approval rules based on adaptive quantitative scoring results, binds specific role electronic signatures at the verification nodes of the multi-level approval rules, and generates review record documents including electronic signatures. The instrument calibration mechanism extracts sensor status characteristics based on the aggregated data stream, compares the sensor status characteristics with preset standard values, generates a periodic sensor calibration command when the deviation between the sensor status characteristics and the preset standard values ​​exceeds a preset range, collects calibration feedback data, verifies the calibration feedback data, and outputs an instrument availability status indicator.

9. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 8, characterized in that, The sensing and acquisition module processes raw sensing messages through a two-level collaborative parsing mechanism, which is as follows: The protocol parsing engine deployed at the system edge receives the raw sensing messages, performs frame synchronization processing, cyclic redundancy check, and byte order alignment conversion on the raw sensing messages, and generates parsed data. Append an absolute timestamp to the parsed data to generate a standard object sequence that includes the absolute timestamp; The streaming data receiving service node deployed in the system cloud receives the standard object sequence transmitted from the system edge side, and uses a pre-built abnormal data detection algorithm to perform abnormal value filtering calculations on the standard object sequence to generate time series data; The time series data is inserted in batches and written to the distributed relational database connected to the communication connection. The time series data is then read from the distributed relational database and aggregated calculations are performed to generate an aggregated data stream.

10. The geological disaster life detection equipment performance evaluation and demonstration system as described in claim 9, characterized in that, The sensing and acquisition module is connected to a remote terminal operation and maintenance container. The remote terminal operation and maintenance container automatically executes the peripheral device long connection pool management logic, disconnection reconnection logic, periodic connection status keep-alive message detection logic, communication link quality prediction logic, and terminal embedded firmware differential upgrade logic.

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