Low-power-consumption management system for wireless detection of goaf

Abstract

The invention relates to the technical field of intelligent regulation and control, in particular to a goaf wireless detection low-power-consumption management system which comprises a parameter initialization module, a data integration module, a safety evaluation module, a strategy response module, a node dormancy regulation and control module and an operation efficiency feedback and parameter tuning module. Performing parameter initialization on the sensing monitoring nodes in the target area to obtain low-power-consumption operation control parameters; acquiring multi-parameter environment data of the target area; performing risk threshold evaluation on the safety state of the target area to obtain a safety evaluation result; performing strategy response on the sensing monitoring nodes to obtain an instruction control strategy; performing adaptive regulation and control on the activity duration and the dormancy duration of the sensing monitoring node to obtain a dormancy regulation and control instruction; monitoring an execution state and energy consumption data of the instruction control strategy and the dormancy regulation and control instruction to obtain an operation efficiency evaluation report to perform synchronous regulation and control on low-power-consumption operation control parameters; according to the invention, the efficiency of low-power-consumption management of wireless detection of the goaf can be improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a low-power management system for wireless detection of goaf areas. Background Technology

[0002] In low-power management of wireless detection in goaf areas, existing technologies lack comprehensive adaptability design for parameter initialization. They fail to adequately consider core factors such as the deployment environment, device attributes, and network roles of sensor monitoring nodes. This results in a lack of scientific basis for configuring initial acquisition frequency, sleep duration, and transmission power, leading to excessive energy consumption or insufficient data acquisition continuity. Furthermore, existing data integration processes fail to effectively address the heterogeneity of various environmental parameters, resulting in inconsistent dimensions and low data structuring. This makes it difficult to extract the necessary information for subsequent safety assessments, hindering accurate data support for risk judgment.

[0003] Existing safety assessment mechanisms suffer from limitations due to their reliance on static thresholds of certain environmental parameters. They fail to comprehensively consider key factors such as the dynamic rate of change of parameters, historical control command execution feedback, and the presence of characteristic gases. This leads to discrepancies between safety assessment results and the actual risk status of the goaf, hindering early warning and accurate risk identification. Furthermore, dormancy control strategies do not prioritize overall network lifetime as their core optimization objective. They lack adaptability to real-time resource conditions such as node remaining energy and routing load, and control commands lack dynamic adjustment capabilities. The absence of closed-loop operational performance feedback and parameter optimization mechanisms makes it difficult to balance low-power control and detection efficiency, resulting in overall low management efficiency. Therefore, improving the low-power management efficiency of wireless detection in goaf areas has become an urgent problem to be solved. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a low-power management system for wireless detection of goaf areas, characterized in that the system includes a parameter initialization module, a data integration module, a security assessment module, a strategy response module, a node sleep control module, and an operational performance feedback and parameter optimization module, wherein: The parameter initialization module is used to initialize the parameters of the sensing and monitoring nodes in the target area to obtain the low-power operation control parameters of the target area. The data integration module is used to integrate the carbon monoxide concentration, temperature, ethylene concentration and acetylene concentration collected in the target area into multi-parameter environmental data of the target area based on the low-power operation control parameters. The safety assessment module is used to assess the safety status of the target area based on the comprehensive factor change rate of the multi-parameter environmental data and the data control log in the sensor monitoring node, and obtain the safety assessment result of the target area. The strategy response module is used to respond to the sensing and monitoring node based on the adaptive data control strategy of the target area and the security assessment result, so as to obtain the instruction control strategy of the sensing and monitoring node. The node sleep control module is used to optimize the overall network lifetime of the sensor monitoring node by taking the maximum network lifetime as the optimization goal, and in combination with the resource status of the sensor monitoring node, to adaptively control the activity time and sleep time of the sensor monitoring node, so as to obtain the sleep control command of the sensor monitoring node. The operation performance feedback and parameter tuning module is used to monitor the execution status and energy consumption data of the command control strategy and the sleep regulation command, so as to obtain the operation performance evaluation report of the sensor monitoring node, and to synchronously regulate the low power operation control parameters based on the operation performance evaluation report.

[0005] In a preferred embodiment, when the parameter initialization module performs parameter initialization on the sensing and monitoring nodes in the target area to obtain the low-power operation control parameters of the target area, it is specifically used for: Based on the type of sensing and monitoring equipment in the target area and the deployment location of the sensing and monitoring nodes in the target area, assign a corresponding initial data acquisition frequency to the sensing and monitoring nodes; Based on the power supply method of the sensor monitoring node, set the initial sleep duration for the sensor monitoring node; Based on the network monitoring topology of the target area and the relay role of the sensor monitoring node, an initial transmission power level is set for the sensor monitoring node; The initial data acquisition frequency, the initial sleep duration, and the initial transmit power level are integrated into the low-power operation control parameters of the sensing and monitoring node.

[0006] In a preferred embodiment, when the data integration module integrates the carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration collected in the target area into multi-parameter environmental data of the target area based on the low-power operation control parameters, it is specifically used for: Based on the data acquisition frequency in the low-power operation control parameters, the carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration of the target area are obtained as the original data sequence of the target area; The original data sequence is standardized in terms of dimensions to obtain standardized parameter data for the target region. The standardized parameter data is combined and encapsulated to obtain a structured data packet for the target region; Based on the region identifier and acquisition time information of the target region, the structured data packet is spliced ​​to obtain multi-parameter environmental data of the target region.

[0007] In a preferred embodiment, when the safety assessment module performs a risk threshold assessment of the safety status of the target area based on the comprehensive factor change rate of the multi-parameter environmental data and the data control logs in the sensor monitoring node, and obtains the safety assessment result of the target area, it is specifically used for: Extract the time-series data of carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration from the multi-parameter environmental data; The average and instantaneous rates of change of the carbon monoxide concentration time series data, and the heating rate of the temperature time series data, are characterized as dynamic change indicators of the multi-parameter environmental data. Retrieve the corresponding historical control command records in the data control log and the execution response status records of the historical commands in the sensor monitoring node; Based on the dynamic change indicators, the historical control command records, the execution response status records, and the ethylene concentration data and acetylene concentration data as Boolean identifiers for the appearance of characteristic gases, a comprehensive evaluation matrix of the multi-parameter environmental data is constructed. Based on the historical impact rule base of the target area, the comprehensive evaluation matrix is ​​correlated and mapped to obtain the security evaluation result of the target area.

[0008] In a preferred embodiment, the dynamic change index is calculated using the following formula: ; In the formula, The aforementioned dynamic change index, The average rate of change in carbon monoxide concentration is the weighting factor. The average rate of change of carbon monoxide concentration in the time-series data of carbon monoxide concentration is given. The instantaneous rate of change of the carbon monoxide concentration is the weighting coefficient. The instantaneous rate of change of the carbon monoxide concentration is given. The preset instantaneous temperature weighting coefficient, The temperature rise rate of the temperature time series data. It is a very small positive number.

[0009] In a preferred embodiment, when the safety assessment module constructs a comprehensive assessment matrix of the multi-parameter environmental data based on the dynamic change indicators, the historical control command records, the execution response status records, and the Boolean identifiers of the ethylene concentration data and acetylene concentration data as characteristic gases, it is specifically used for: Tensor synthesis is performed on the dynamic change indicators to obtain the environmental parameter vector of the target area; The instruction types in the historical control instruction record are correlated and quantified with the status feedback in the execution response status record to obtain the historical control vector of the target area. Based on the characteristic gas concentration threshold of the target area, the ethylene concentration data and acetylene concentration data are compared by threshold, and the Boolean identifiers of the ethylene concentration data and acetylene concentration data are determined according to the comparison results. The environmental parameter vector, the historical control vector, and the Boolean identifier are reconstructed to obtain the comprehensive evaluation matrix of the target area.

[0010] In a preferred embodiment, when the policy response module executes an adaptive data control policy based on the target area and the security assessment result to respond to the sensing monitoring node and obtain the instruction control policy for the sensing monitoring node, it is specifically used for: Based on the adaptive data control strategy of the target area, the security assessment results are mapped and corrected to obtain the basic response strategy framework of the sensing and monitoring node. Combining the security assessment results with the real-time resource status of the sensing and monitoring node, the action parameters in the basic response strategy framework are instantiated and assigned values ​​to obtain the preliminary control instruction set of the sensing and monitoring node. The logical consistency and resource conflicts of the preliminary control instruction set are checked and detected, and the checked instruction set is converted into the instruction control strategy of the sensing and monitoring node.

[0011] In a preferred embodiment, when the node sleep control module executes an optimization objective of maximizing the overall network lifetime of the sensor monitoring node and, in conjunction with the resource status of the sensor monitoring node, adaptively controls the activity duration and sleep duration of the sensor monitoring node to obtain the sleep control instruction for the sensor monitoring node, it is specifically used for: The ratio between the active duration and the sleep duration of the sensor monitoring node is used as the controlled variable. The remaining energy status and routing load of the sensing and monitoring nodes are used as feedback input signals; With the goal of maximizing the overall network lifetime of the sensor monitoring node, Pareto analysis is performed on the sleep strategy library of the sensor monitoring node based on the feedback input signal and the controlled variable to obtain the optimized sleep strategy set of the sensor monitoring node. Based on the optimized sleep strategy set, the sensor monitoring node is parameterized to obtain the sleep energy consumption control parameters of the sensor monitoring node. The sleep energy consumption control parameters are encapsulated and encoded to obtain the sleep control command of the sensing and monitoring node.

[0012] In a preferred embodiment, when the operation performance feedback and parameter tuning module executes the monitoring of the command control strategy and the sleep regulation command's execution status and energy consumption data to obtain the operation performance evaluation report of the sensor monitoring node, and synchronously regulates the low-power operation control parameters based on the operation performance evaluation report, it is specifically used for: The response delay data and execution success rate data generated during the actual execution of the command control strategy are used as the execution status dataset of the sensing and monitoring node; The node activation duration data and sleep-wake interval data corresponding to the hibernation control command within the execution cycle are used as the energy consumption monitoring dataset of the sensing monitoring node. The performance of the execution status dataset and the energy consumption monitoring dataset is predicted to obtain the operational performance evaluation report of the sensor monitoring node. Based on the operational performance evaluation report, the low-power operation control parameters are adaptively adjusted to complete the synchronous control of the sensing and monitoring nodes.

[0013] In a preferred embodiment, when the performance feedback and parameter tuning module performs adaptive adjustments to the low-power operation control parameters based on the performance evaluation report to complete the synchronous control of the sensing and monitoring nodes, it is specifically used for: When the operational performance evaluation report shows that the system energy consumption is too high and the operational performance is good, the sampling frequency benchmark value of the sensor monitoring node and the sleep duration benchmark value shall be appropriately reduced without reducing the operational performance. If the performance evaluation report shows that the system performance is insufficient but the energy consumption is low, the baseline value of the acquisition frequency of the sensor monitoring node should be appropriately increased and the baseline value of the sleep time should be shortened. If the operational performance evaluation report shows a decrease in network connectivity, then the baseline value of the transmit power of the key nodes in the sensing and monitoring nodes shall be increased. The adjusted acquisition frequency reference value and the sleep duration reference value will be used as the basis for initializing the parameters in the next cycle.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention employs a multi-dimensional adaptive parameter initialization design, combining core factors such as the device type, deployment location, power supply method, and network role of the sensing and monitoring nodes to precisely configure the initial acquisition frequency, sleep duration, and transmission power, laying a solid foundation for low-power operation. During data integration, standardized multi-parameter environmental data is formed through unified dimensions, structured encapsulation, and the splicing of time and regional identifiers, providing high-quality data support for subsequent safety assessments. In the safety assessment stage, a comprehensive assessment matrix is ​​constructed by integrating multi-parameter dynamic change indicators, historical control logs, and characteristic gas identifiers. Relying on a historical impact rule base, correlation mapping is achieved, significantly improving the accuracy and comprehensiveness of the target area's safety status assessment.

[0015] 2. This invention, through a strategy response module, completes the basic framework construction, parameter instantiation and assignment, and instruction verification based on adaptive data control strategies and security assessment results, ensuring that the generated instruction control strategies possess logical consistency and resource adaptability. Node sleep regulation aims to maximize the overall network lifetime. By combining node remaining energy and routing load with Pareto analysis, it achieves adaptive regulation of activity and sleep duration, effectively optimizing node energy consumption allocation. The operational performance feedback and parameter tuning module generates evaluation reports by monitoring execution status and energy consumption data, dynamically adjusting low-power control parameters to form a closed-loop regulation mechanism. This continuously improves the system's low-power operation stability and detection efficiency, significantly enhancing the overall efficiency of low-power wireless detection management in goaf areas. Attached Figure Description

[0016] Figure 1 A system architecture diagram of a low-power management system for wireless detection of goaf areas provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments belong to some, but not all, embodiments of the present invention. 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.

[0018] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0019] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0020] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0021] In practice, the server-side equipment deployed in a low-power management system for wireless detection of goaf areas may consist of one or more devices. This low-power management system for wireless detection of goaf areas can be implemented as: a service instance, a virtual machine, or hardware devices. For example, this low-power management system for wireless detection of goaf areas can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, this low-power management system for wireless detection of goaf areas can be understood as software deployed on a cloud node, used to provide a low-power management system for wireless detection of goaf areas to various user terminals. Alternatively, this low-power management system for wireless detection of goaf areas can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this low-power management system for wireless detection of goaf areas can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide a low-power management system for wireless detection of goaf areas to various user terminals.

[0022] In terms of implementation, a low-power management system for wireless detection of goaf areas and a user terminal are mutually compatible. That is, if the low-power management system for wireless detection of goaf areas is implemented as an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the low-power management system for wireless detection of goaf areas is implemented as a website, then the user terminal is implemented as a webpage; or if the low-power management system for wireless detection of goaf areas is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0023] like Figure 1 The figure shown is a system architecture diagram of a low-power management system for wireless detection of goaf areas provided in an embodiment of the present invention.

[0024] The low-power management system 100 for wireless detection of goaf areas described in this invention can be installed on a cloud server. In terms of implementation, it can function as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed into a website. Depending on the functions implemented, the low-power management system 100 for wireless detection of goaf areas may include a parameter initialization module 101, a data integration module 102, a security assessment module 103, a policy response module 104, a node sleep control module 105, and a performance feedback and parameter optimization module 106. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0025] In this embodiment of the invention, in a low-power management system for wireless detection of goaf areas, each of the above-mentioned modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the low-power management system for wireless detection of goaf areas provided by this embodiment of the invention, without modifying the program code, the applicable scope of the architecture of the low-power management system for wireless detection of goaf areas can be adjusted by adding modules and directly calling them, achieving cluster-based horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the low-power management system for wireless detection of goaf areas. In practical applications, the above-mentioned modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0026] The following describes, with reference to specific embodiments, each component and its specific workflow of a low-power management system for wireless detection of goaf areas: The parameter initialization module 101 is used to initialize the parameters of the sensing and monitoring nodes in the target area to obtain the low-power operation control parameters of the target area. In this embodiment of the invention, when the parameter initialization module performs parameter initialization on the sensing and monitoring nodes in the target area to obtain the low-power operation control parameters of the target area, it is specifically used for: Based on the type of sensing and monitoring equipment in the target area and the deployment location of the sensing and monitoring nodes in the target area, assign a corresponding initial data acquisition frequency to the sensing and monitoring nodes; Based on the power supply method of the sensor monitoring node, set the initial sleep duration for the sensor monitoring node; Based on the network monitoring topology of the target area and the relay role of the sensor monitoring node, an initial transmission power level is set for the sensor monitoring node; The initial data acquisition frequency, the initial sleep duration, and the initial transmit power level are integrated into the low-power operation control parameters of the sensing and monitoring node.

[0027] First, clearly define the specific device types of all sensing and monitoring nodes in the target area, including temperature sensors, humidity sensors, vibration sensors, and gas sensors. Simultaneously, conduct on-site surveys to record the precise deployment location of each sensing and monitoring node within the target area, such as the core monitoring area, edge auxiliary monitoring area, densely populated equipment area, and open area. For temperature sensors, if deployed in the core monitoring area and requiring real-time capture of ambient temperature fluctuations, the initial data acquisition frequency is real-time acquisition. If the temperature sensor is deployed in the edge auxiliary monitoring area where ambient temperature changes are relatively gradual, the initial data acquisition frequency is periodic low-frequency acquisition. For vibration sensors, if deployed in densely populated equipment areas to monitor equipment operating status, the initial data acquisition frequency is high-frequency periodic acquisition. For gas sensors, if deployed in open areas where gas concentration changes slowly, the initial data acquisition frequency is long-cycle low-frequency acquisition. This ensures that the allocation of initial data acquisition frequencies perfectly matches the functional requirements of the sensing and monitoring device types and the environmental characteristics of the deployment locations, achieving precise control of the data acquisition rhythm in different scenarios.

[0028] Each sensor monitoring node's power supply method was verified to determine whether it was wired or battery-powered. For battery-powered nodes, the battery capacity level needed further confirmation, such as small-capacity, medium-capacity, or large-capacity batteries. For wired-powered sensor monitoring nodes, since the power supply is stable and there is no need to worry about power consumption, the initial sleep time was set to a short sleep period after each data acquisition. For battery-powered sensor monitoring nodes with small-capacity batteries, considering their power consumption during data acquisition, a longer initial sleep time was set after each data acquisition to ensure stable operation over a long period. For battery-powered sensor monitoring nodes with large-capacity batteries, a medium-length initial sleep time was set after each data acquisition. This ensures that the initial sleep time setting is directly related to the stability of the power supply method and the battery capacity, and power consumption is optimized by adjusting the sleep time.

[0029] Analyze the network monitoring topology of the target area by analyzing the topology diagram to determine whether it is a star topology, mesh topology, or tree topology, etc. At the same time, determine whether each sensor monitoring node plays a relay role based on the functional positioning of the nodes in the network, and specifically whether it is a primary relay node, a secondary relay node, or a non-relay node. If the network monitoring topology is a star topology, the core node, acting as a primary relay node, needs to establish communication connections with all terminal nodes within the target area. The initial transmit power setting is set to a high power level, which covers the entire communication range of the star topology, ensuring that data from all terminal nodes can be effectively transmitted to the core node. Terminal nodes in the star topology, acting as non-relay nodes, only need to communicate unidirectionally with the core node. The initial transmit power setting is set to a low power level, which meets the direct communication needs with the core node. If the network monitoring topology is a mesh topology, the secondary relay node, responsible for data forwarding, needs to interact with multiple adjacent nodes. The initial transmit power setting is set to a medium power level, which ensures stable communication between adjacent nodes while avoiding energy waste caused by excessive power. Precise setting of the transmit power level achieves a balance between communication efficiency and power consumption.

[0030] The initial data acquisition frequency, initial sleep duration, and initial transmit power level of each identified sensor monitoring node are correlated one-to-one and recorded in the format of "node number - initial data acquisition frequency - initial sleep duration - initial transmit power level" to ensure accurate matching and no omissions of the three parameters for each node. For any numbered sensor monitoring node, its corresponding initial data acquisition frequency, initial sleep duration, and initial transmit power level are bound to form a unique operation control combination for that node. This ultimately generates complete low-power operation control parameters for each sensor monitoring node, providing a clear execution basis for the low-power operation control of subsequent nodes and achieving systematic control of the sensor monitoring node's operating status.

[0031] The beneficial effects include accurately matching the functional requirements of sensor monitoring equipment with the environmental characteristics of the deployment location, rationally allocating the initial data acquisition frequency, setting an appropriate initial sleep duration based on the stability of the power supply method and the battery capacity level, setting an appropriate initial transmission power level based on the network monitoring topology and relay role, and forming exclusive low-power operation control parameters by integrating the three parameters. This enables systematic and precise control of the operating status of sensor monitoring nodes, optimizes node power consumption while ensuring communication efficiency and data acquisition effectiveness, and helps nodes to operate stably for a long time.

[0032] The data integration module 102 is used to integrate the carbon monoxide concentration, temperature, ethylene concentration and acetylene concentration collected in the target area into multi-parameter environmental data of the target area based on the low power operation control parameters. In this embodiment of the invention, when the data integration module integrates the carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration collected in the target area into multi-parameter environmental data of the target area based on the low-power operation control parameters, it is specifically used for: Based on the data acquisition frequency in the low-power operation control parameters, the carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration of the target area are obtained as the original data sequence of the target area; The original data sequence is standardized in terms of dimensions to obtain standardized parameter data for the target region. The standardized parameter data is combined and encapsulated to obtain a structured data packet for the target region; Based on the region identifier and acquisition time information of the target region, the structured data packet is spliced ​​to obtain multi-parameter environmental data of the target region.

[0033] Based on the preset data acquisition frequency in the low-power operation control parameters, the sensors responsible for collecting carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration in the target area start data acquisition respectively. Each sensor strictly follows the acquisition frequency to continuously capture the corresponding environmental parameters. After each acquisition is completed, the current parameter value is recorded immediately, and the time point of the acquisition action is recorded simultaneously. All carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration values ​​recorded in chronological order are sorted and summarized to form the original data sequence of the target area containing complete acquisition records of the four environmental parameters.

[0034] The data on carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration in the original data sequence were individually verified to clarify the original dimension type of each parameter. The original dimensions of carbon monoxide, ethylene, and acetylene concentrations were expressed as volume fractions, while the original dimension of temperature was degrees Celsius. By determining the value range of each parameter under common environmental conditions in the target area, and using the maximum value of this range as a reference, the specific value of each parameter in the original data sequence was compared with the corresponding reference value. This ensured that the calculated results of all parameters fell within the same dimensionless value range. Through this unified processing, standardized parameter data for the target area were obtained.

[0035] Following a fixed order of "carbon monoxide concentration - temperature - ethylene concentration - acetylene concentration," the four categories of standardized parameter data are arranged sequentially. A unique type identifier is added to each category, directly identifying the parameter attribute and preventing confusion between different types of data. All the ordered, type-identified standardized parameter data are integrated into a single data unit. This unit is then encapsulated using a pre-defined data packet format, ensuring its integrity and independence during encapsulation. This process ultimately forms a structured data packet for the target region.

[0036] A unique area identifier for the target area is pre-acquired to clearly distinguish different monitoring areas. Simultaneously, the acquisition time information corresponding to the structured data packet is extracted, with the acquisition time accurate to the specific moment the acquisition action was completed. The area identifier, acquisition time information, and structured data packet are then associated and integrated, and the data is concatenated according to a fixed logical order of "area identifier – acquisition time information – structured data packet." This ensures a one-to-one correspondence between the area identifier, acquisition time information, and environmental parameter data in the structured data packet. Through this concatenation process, multi-parameter environmental data for the target area that clearly corresponds to the target area and acquisition time is ultimately obtained.

[0037] The beneficial effects include ensuring the integrity and timeliness of the original data sequence by strictly adhering to low-power operation control parameters, eliminating parameter differences through unified dimensions to ensure the consistency of standardized parameter data, and enabling structured data packets to have clear and orderly internal logic through combination and encapsulation. By combining area identifiers and acquisition time, multi-parameter environmental data can be accurately associated with the monitoring area and acquisition time, providing standardized, complete, and traceable reliable data support for a comprehensive and accurate assessment of the environmental status of the target area.

[0038] The safety assessment module 103 is used to assess the safety status of the target area based on the comprehensive factor change rate of the multi-parameter environmental data and the data control log in the sensor monitoring node, and obtain the safety assessment result of the target area. In this embodiment of the invention, when the safety assessment module performs a risk threshold assessment of the safety status of the target area based on the comprehensive factor change rate of the multi-parameter environmental data and the data control log in the sensor monitoring node, and obtains the safety assessment result of the target area, it is specifically used for: Extract the time-series data of carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration from the multi-parameter environmental data; The average and instantaneous rates of change of the carbon monoxide concentration time series data, and the heating rate of the temperature time series data, are characterized as dynamic change indicators of the multi-parameter environmental data. Retrieve the corresponding historical control command records in the data control log and the execution response status records of the historical commands in the sensor monitoring node; Based on the dynamic change indicators, the historical control command records, the execution response status records, and the ethylene concentration data and acetylene concentration data as Boolean identifiers for the appearance of characteristic gases, a comprehensive evaluation matrix of the multi-parameter environmental data is constructed. Based on the historical impact rule base of the target area, the comprehensive evaluation matrix is ​​correlated and mapped to obtain the security evaluation result of the target area.

[0039] The calculation formula for the dynamic change index is as follows: ; In the formula, The aforementioned dynamic change index, The average rate of change in carbon monoxide concentration is the weighting factor. The average rate of change of carbon monoxide concentration in the time-series data of carbon monoxide concentration is given. The instantaneous rate of change of the carbon monoxide concentration is the weighting coefficient. The instantaneous rate of change of the carbon monoxide concentration is given. The preset instantaneous temperature weighting coefficient, The temperature rise rate of the temperature time series data. It is a very small positive number.

[0040] When the safety assessment module constructs a comprehensive assessment matrix of the multi-parameter environmental data based on the dynamic change indicators, the historical control command records, the execution response status records, and the Boolean identifiers of the ethylene and acetylene concentration data as characteristic gases, it is specifically used for: Tensor synthesis is performed on the dynamic change indicators to obtain the environmental parameter vector of the target area; The instruction types in the historical control instruction record are correlated and quantified with the status feedback in the execution response status record to obtain the historical control vector of the target area. Based on the characteristic gas concentration threshold of the target area, the ethylene concentration data and acetylene concentration data are compared by threshold, and the Boolean identifiers of the ethylene concentration data and acetylene concentration data are determined according to the comparison results. The environmental parameter vector, the historical control vector, and the Boolean identifier are reconstructed to obtain the comprehensive evaluation matrix of the target area.

[0041] Structured data packages were extracted from the multi-parameter environmental data of the target area. These packages contained standardized parameter data for carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration, each with a unique type identifier. The standardized data for each parameter were sorted according to the acquisition time. All standardized data labeled as carbon monoxide concentration were selected and arranged into a continuous sequence according to time, forming time-series data for carbon monoxide concentration. Similarly, all standardized data labeled as temperature were selected and sorted by time, forming time-series data for temperature. All standardized data labeled as ethylene concentration were directly extracted from the structured data package to form ethylene concentration data. Simultaneously, all standardized data labeled as acetylene concentration were extracted from the structured data package to form acetylene concentration data, ensuring that the extracted four types of data completely correspond to the original parameter information in the multi-parameter environmental data.

[0042] The carbon monoxide concentration time-series data are arranged chronologically according to the acquisition time. The concentration values ​​corresponding to all two adjacent acquisition time points in the sequence are selected, and the difference between the subsequent value and the preceding value is calculated to obtain the carbon monoxide concentration change within each time interval. The concentration changes of all time intervals are summed and then divided by the total number of time intervals to obtain the average rate of change of the carbon monoxide concentration time-series data. For each adjacent acquisition time point in the carbon monoxide concentration time-series data, the difference between the concentration values ​​of the subsequent acquisition point and the preceding acquisition point is directly calculated. Each difference is the instantaneous rate of change of the carbon monoxide concentration time-series data at the corresponding time point. The temperature time-series data are arranged chronologically according to the acquisition time, and the difference between the temperature values ​​corresponding to two adjacent acquisition time points is calculated. This difference is the temperature change within the corresponding time interval, and the resulting temperature change is the temperature rise rate of the temperature time-series data. The average rate of change and instantaneous rate of change of the carbon monoxide concentration time-series data, as well as the temperature rise rate of the temperature time-series data, are collectively determined as the dynamic change index of the multi-parameter environmental data.

[0043] The data control log storing the operational information of the sensor monitoring nodes is retrieved. This log records all control commands issued to the sensor monitoring nodes for the target area and the execution feedback information of the nodes in chronological order. Based on the acquisition time range corresponding to the multi-parameter environmental data, all records related to the control commands issued within that time range are filtered out from the data control log, including the target of the control command, the content of the command, and the issuance time, forming a historical control command record. At the same time, the feedback information of the sensor monitoring nodes corresponding to each historical control command is matched in the data control log to clarify whether each command was received by the node, whether it was executed as required, and whether any abnormalities occurred during the execution process, forming an execution response status record of the historical commands in the sensor monitoring nodes, ensuring that the two types of records correspond one-to-one and completely cover the target time range.

[0044] Ethylene concentration data is checked one by one to determine if any non-zero values ​​exist. If a non-zero value exists, the Boolean flag indicating the ethylene concentration data as a characteristic gas is set to "yes"; if all values ​​are zero, the Boolean flag is set to "no". Acetylene concentration data is processed using the same check method, and the Boolean flag indicating the acetylene concentration data as a characteristic gas is set to "yes" or "no" depending on whether a non-zero value exists. The row dimensions of the comprehensive evaluation matrix are determined to be dynamic change indicators, historical control command records, execution response status records, ethylene concentration Boolean flags, and acetylene concentration Boolean flags. The column dimensions are the corresponding data acquisition time segments. The average rate of change, instantaneous rate of change, and heating rate in the dynamic change indicators are sequentially filled into the corresponding column positions of the row according to the time segments. The historical control command records are split into time segments and filled into the corresponding column positions of the row. The execution response status records are matched one-to-one with the historical control command records and filled into the corresponding column positions of the row. The ethylene concentration Boolean flags and acetylene concentration Boolean flags are filled into the corresponding column positions of the row according to the time segments. Finally, a comprehensive evaluation matrix of multi-parameter environmental data is formed.

[0045] The historical impact rule base for the target area stores the correspondence between different dynamic change index ranges, historical control command types, execution response status results, characteristic gas Boolean identifier combinations, and the safety status of the target area. This correspondence is based on historical safety event cases and long-term monitoring data. The dynamic change index information, historical control command records, execution response status records, and ethylene and acetylene Boolean identifier status corresponding to each column in the comprehensive assessment matrix are combined to form comprehensive feature information for each column. The rule entries in the historical impact rule base that are completely consistent with the comprehensive feature information of each column are searched. The safety status corresponding to the rule entry is the regional safety situation for that time segment. The safety situations of all time segments are integrated to form a complete safety status judgment covering the entire monitoring period of the target area, and finally, the safety assessment result of the target area is obtained.

[0046] The weighting coefficient of the average rate of change of carbon monoxide concentration is determined based on the importance of this indicator in the assessment of dynamic changes. When determining it, a large amount of historical data containing changes in carbon monoxide concentration and corresponding dynamic events is collected. The contribution ratio of the average rate of change of carbon monoxide concentration in different dynamic events is analyzed. The contribution ratio of all indicators is normalized so that the sum of the weighting coefficients is a fixed benchmark. The higher the contribution ratio of the average rate of change, the larger the corresponding normalized value. This value is the weighting coefficient of the average rate of change of carbon monoxide concentration.

[0047] The average rate of change of carbon monoxide concentration is calculated using time-series data of carbon monoxide concentration. The calculation first defines the statistical time range of the time-series data, extracts the carbon monoxide concentration values ​​at the start and end times within that range, subtracts the concentration value at the start time from the concentration value at the end time to obtain the total change in concentration, and then divides the total change in concentration by the duration of the statistical time range. The result is the average rate of change of carbon monoxide concentration.

[0048] The weighting coefficient for the instantaneous change rate of carbon monoxide concentration is set based on the influence of this indicator in dynamic change monitoring. When setting it, historical monitoring data is referenced, and the correlation between the instantaneous change rate of carbon monoxide concentration and the probability of dynamic events is statistically analyzed. The higher the correlation, the more sensitive the indicator is to dynamic changes. The correlation is quantified into a specific value, which is the weighting coefficient for the instantaneous change rate of carbon monoxide concentration. This coefficient is also included in the normalization process of all weighting coefficients to ensure overall weight balance.

[0049] The instantaneous rate of change of carbon monoxide concentration is calculated from the concentration data of adjacent sampling points. The concentration values ​​of two consecutive adjacent sampling points in the time series data of carbon monoxide concentration are obtained. The concentration value of the previous sampling point is subtracted from the concentration value of the latter sampling point to obtain the concentration difference between the adjacent sampling points. Then, the concentration difference is divided by the time interval between the two sampling points. The result is the instantaneous rate of change of carbon monoxide concentration. This indicator reflects the intensity of the instantaneous change in concentration at a certain moment.

[0050] The preset instantaneous temperature weighting coefficient is set based on the correlation between the temperature rise rate and dynamic changes. When setting it, a large amount of collaborative data on temperature changes and carbon monoxide concentration changes are analyzed to clarify the promoting or inhibiting effect of the temperature rise rate on the development of dynamic events. The strength of this effect is quantified into a specific value, which is the preset instantaneous temperature weighting coefficient. After normalization, this coefficient, together with the other two weighting coefficients, constitutes a complete weighting system.

[0051] The temperature rise rate of temperature time series data is calculated from the temperature time series data. The temperature values ​​of two consecutive adjacent sampling points are extracted from the temperature time series data. The temperature value of the previous sampling point is subtracted from the temperature value of the latter sampling point to obtain the temperature difference between the adjacent sampling points. Then, the temperature difference is divided by the time interval between the two sampling points. The result is the temperature rise rate of the temperature time series data. This indicator reflects the magnitude of temperature change per unit time.

[0052] The minimum positive number is a fixed positive number that is set in advance. When setting it, a value much smaller than the normal value of the denominator is selected by referring to the range of all possible calculation results of the denominator. Its purpose is to avoid calculation abnormalities caused by the calculation result of the denominator being zero, and to ensure that the calculation process of dynamically changing indicators is always effective. Once this value is set, it remains unchanged in all calculation scenarios.

[0053] The dynamic change index is a quantitative indicator that comprehensively measures the intensity of dynamic changes under the combined effects of average and instantaneous changes in carbon monoxide concentration and the rate of temperature rise. Its value directly reflects the activity level of relevant dynamic events in the current environment.

[0054] The molecular part integrates the effects of three core indicators through weighted summation: the average rate of change of carbon monoxide concentration is multiplied by the corresponding weight coefficient to highlight the impact of long-term concentration change trends on dynamic assessment; the instantaneous rate of change of carbon monoxide concentration is multiplied by the corresponding weight coefficient to emphasize the role of immediate concentration fluctuations; and the rate of temperature rise is multiplied by the preset instantaneous temperature weight coefficient to reflect the synergistic effect of temperature changes on the dynamic process. The sum of the three comprehensively reflects the dynamic intensity of the combined effect of various factors.

[0055] The denominator is normalized by taking the square root of the sum of the squares of the three core indicators and the smallest positive constant, thus avoiding evaluation bias caused by differences in the magnitude of the values ​​of the indicators. At the same time, the smallest positive constant ensures that the denominator is always positive, guaranteeing the validity of the calculation.

[0056] The larger the value of the dynamic change index, the stronger the combined effect of the average change and instantaneous change of carbon monoxide concentration and the rate of temperature rise, and the higher the activity level of the corresponding dynamic event; the smaller the value, the weaker the combined effect and the smoother the dynamic event. This index provides accurate quantitative basis for scenarios such as environmental dynamic monitoring and risk early warning.

[0057] The dynamic change indicators are clearly defined as the average rate of change and instantaneous rate of change of carbon monoxide concentration time series data, and the temperature rise rate of temperature time series data. First, a fixed arrangement order of the three types of indicators is determined. Taking the time dimension as the core clue, the average rate of change, instantaneous rate of change, and temperature rise rate corresponding to the same collection time segment are extracted in sequence and linearly integrated in the order of "average rate of change - instantaneous rate of change - temperature rise rate". Each collection time segment forms an ordered set of indicator combinations. The indicator combinations of all collection time segments are concatenated in chronological order to form a two-dimensional data set containing the time dimension and the indicator dimension. Through this tensor synthesis process, the environmental parameter vector of the target area is obtained.

[0058] All command types in the historical control command records are analyzed, including data acquisition frequency adjustment commands, sleep duration modification commands, and transmit power level switching commands. At the same time, the status feedback in the execution response status records is extracted, including command execution success, command execution failure, and command partial execution. A one-to-one correspondence between command types and status feedback is established, and a unique character symbol is assigned to each combination of command type and corresponding status feedback. According to the issuance time sequence of historical control commands, all the character symbols corresponding to the combinations are arranged sequentially to form a continuous one-dimensional data sequence. Through this correlation and quantization process, the historical control vector of the target area is obtained.

[0059] A threshold for the concentration of characteristic gases in the target area is preset. This threshold is a critical concentration value determined based on regional safety standards and is used to determine whether the characteristic gas has reached a level that may affect safety. The concentration values ​​of each collection time point in the ethylene concentration data are extracted one by one, and each value is compared with the characteristic gas concentration threshold. If the concentration value is greater than or equal to the characteristic gas concentration threshold, the Boolean flag indicating that the ethylene concentration data at that collection time point is a characteristic gas is set to "yes"; if the concentration value is less than the characteristic gas concentration threshold, the Boolean flag is set to "no". The same comparison method is used to compare the concentration values ​​of each collection time point in the acetylene concentration data with the characteristic gas concentration threshold one by one, and the Boolean flag indicating whether the acetylene concentration data is a characteristic gas is set to "yes" or "no" based on the comparison results.

[0060] First, we define the environmental parameter vector as a two-dimensional dataset, the historical control vector as a one-dimensional data sequence, and the Boolean identifiers as two independent logical judgment results. By expanding the dimensions, we complete the historical control vector into a two-dimensional dataset consistent with the environmental parameter vector in terms of time dimension. At the same time, we expand each of the two Boolean identifiers into a single-dimensional two-dimensional dataset, ensuring that the environmental parameter vector, historical control vector, and Boolean identifiers are fully aligned in time dimension. Using time segments as columns and the three types of data as rows, we concatenate the two-dimensional datasets corresponding to the environmental parameter vector, historical control vector, and Boolean identifiers in the order of "environmental parameter vector - historical control vector - Boolean identifier" to form a multi-dimensional data array containing time dimension, indicator dimension, control dimension, and characteristic gas dimension. Through this dimensional reconstruction process, we obtain the comprehensive evaluation matrix of the target area.

[0061] The beneficial effects include the accurate extraction of various time-series and characteristic gas data from multi-parameter environmental data, the construction of indicators that reflect environmental dynamics, the complete acquisition of historical control commands and execution response records related to node operation, the formation of a comprehensive evaluation matrix by integrating multi-dimensional information, and the realization of accurate correlation mapping between the matrix and safety status by combining the historical impact rule base. Ultimately, an evaluation result that covers the entire monitoring period and reflects the true safety situation of the region is obtained, providing comprehensive and reliable data support for the assessment of safety risks in the target area.

[0062] By precisely integrating dynamic indicators through tensor synthesis to form an environmental parameter vector, and quantifying the correlation between historical control command types and execution response status to obtain a complete historical control vector, the Boolean identifier of characteristic gases is identified by comparing characteristic gas concentration thresholds. Through dimensional reconstruction, the time dimension alignment and effective fusion of multiple types of data are achieved, and finally a comprehensive evaluation matrix that fully reflects environmental dynamics, node control, and characteristic gas conditions is constructed, providing a data foundation with a clear structure and complete information for accurate assessment of the safety status of the target area.

[0063] The strategy response module 104 is used to respond to the sensing and monitoring node based on the adaptive data control strategy of the target area and the security assessment result, so as to obtain the instruction control strategy of the sensing and monitoring node. In this embodiment of the invention, when the policy response module executes an adaptive data control policy based on the target area and the security assessment result to respond to the sensing monitoring node and obtain the instruction control policy of the sensing monitoring node, it is specifically used for: Based on the adaptive data control strategy of the target area, the security assessment results are mapped and corrected to obtain the basic response strategy framework of the sensing and monitoring node. Combining the security assessment results with the real-time resource status of the sensing and monitoring node, the action parameters in the basic response strategy framework are instantiated and assigned values ​​to obtain the preliminary control instruction set of the sensing and monitoring node. The logical consistency and resource conflicts of the preliminary control instruction set are checked and detected, and the checked instruction set is converted into the instruction control strategy of the sensing and monitoring node.

[0064] The adaptive data control strategy based on the target area includes preset response principles corresponding to different security states, such as the low-power maintenance principle for a safe state, the dynamic monitoring principle for a low-risk state, the enhanced monitoring principle for a medium-risk state, and the emergency response principle for a high-risk state. The strategy precisely matches the security state of the target area, as determined by the security assessment, with the preset response principles in the adaptive data control strategy. If the security assessment result is a safe state, the low-power maintenance principle is matched; if it is a low-risk state, the dynamic monitoring principle is matched; if it is a medium-risk state, the enhanced monitoring principle is matched; and if it is a high-risk state, the emergency response principle is matched. Based on the matched response principles, the types of actions that the sensor monitoring nodes need to perform are determined, including adjusting the data acquisition frequency, modifying the sleep duration, switching the transmission power level, and setting the data transmission priority. These action types are then sorted according to the response logic order to form a structured framework containing action types, execution logic, and priority order. Through this mapping and correction process, the basic response strategy framework for the sensor monitoring nodes is obtained.

[0065] Real-time resource status information of the sensor monitoring node is collected, including remaining power, communication link load, remaining storage capacity, and hardware operating temperature. This real-time resource status information is jointly analyzed with the security assessment results. If the security assessment result is low-risk and the node has sufficient remaining power and low communication link load, the data acquisition frequency adjustment action in the basic response strategy framework is assigned a specific rhythm of high-frequency acquisition, the sleep duration modification action is assigned a specific requirement of short-duration sleep, and the transmit power level switching action is assigned a specific setting of medium power level. If the security assessment result is low-risk but the node has insufficient remaining power and high communication link load, the data acquisition frequency adjustment action is assigned a specific rhythm of medium-frequency acquisition, the sleep duration modification action is assigned a specific requirement of medium-duration sleep, and the transmit power level switching action is assigned a specific setting of low power level. All action types in the basic response strategy framework are assigned clear and specific action parameters in the above manner, combining the security assessment results and real-time resource status. All parameterized actions are organized into an ordered set according to the execution order to obtain the preliminary control command set of the sensor monitoring node.

[0066] Each instruction in the preliminary control instruction set is checked one by one to determine whether there are any logical contradictions between different instructions. For example, it is checked whether there are conflicting execution requirements such as "increasing the data acquisition frequency" and "extending the sleep time" at the same time, ensuring that all instructions are logically compatible and non-contradictory. At the same time, it is checked whether the action corresponding to each instruction exceeds the resource carrying capacity of the sensor monitoring node. For example, it is checked whether the combination instruction of "high-frequency acquisition + high-power transmission" will cause the node's remaining power to be depleted quickly, or whether it will exceed the maximum transmission load of the communication link or the maximum storage capacity of the storage unit. If logical contradictions or resource conflicts are found, the core instructions based on the security assessment results are retained first, and the action parameters of secondary instructions are adjusted, such as reducing the power consumption or acquisition frequency of non-core instructions, to eliminate conflicts. After completing the verification and detection of logical consistency and resource conflicts, each instruction in the preliminary control instruction set that has passed the verification is converted into an instruction format that the node can directly recognize and execute, according to the hardware instruction parsing rules of the sensor monitoring node. This includes key information such as instruction code, execution sequence, and trigger conditions, ultimately obtaining the instruction control strategy of the sensor monitoring node.

[0067] The beneficial effects are: based on the accurate mapping and correction of adaptive data control strategies and security assessment results, a basic response strategy framework with clear logic and well-defined priorities is formed; the action parameters in the framework are assigned targeted values ​​in combination with the real-time resource status of the nodes, ensuring that the initial control instruction set not only meets security requirements but also adapts to resource carrying capacity; execution contradictions are eliminated through strict verification of logical consistency and resource conflicts; and instruction control strategies that can be directly executed by the nodes are obtained through format conversion, realizing a dynamic balance between security response and resource optimization, and ensuring that the sensing and monitoring nodes operate efficiently and stably under different security conditions.

[0068] The node sleep control module 105 is used to adaptively control the activity duration and sleep duration of the sensor monitoring node with the optimization goal of maximizing the overall network lifetime of the sensor monitoring node and in combination with the resource status of the sensor monitoring node, so as to obtain the sleep control command of the sensor monitoring node. In this embodiment of the invention, when the node sleep control module executes an optimization objective of maximizing the overall network lifetime of the sensor monitoring node and, in conjunction with the resource status of the sensor monitoring node, adaptively controls the activity duration and sleep duration of the sensor monitoring node to obtain the sleep control instruction for the sensor monitoring node, it is specifically used for: The ratio between the active duration and the sleep duration of the sensor monitoring node is used as the controlled variable. The remaining energy status and routing load of the sensing and monitoring nodes are used as feedback input signals; With the goal of maximizing the overall network lifetime of the sensor monitoring node, Pareto analysis is performed on the sleep strategy library of the sensor monitoring node based on the feedback input signal and the controlled variable to obtain the optimized sleep strategy set of the sensor monitoring node. Based on the optimized sleep strategy set, the sensor monitoring node is parameterized to obtain the sleep energy consumption control parameters of the sensor monitoring node. The sleep energy consumption control parameters are encapsulated and encoded to obtain the sleep control command of the sensing and monitoring node.

[0069] The activity duration of the sensor monitoring node is defined as the duration for which the node performs necessary functions such as data acquisition, data transmission, and command response. The sleep duration is defined as the duration for which the node shuts down unnecessary hardware modules and stops non-core functions to reduce energy consumption. The ratio between the activity duration and the sleep duration is calculated to determine the proportional relationship between the two. This proportional relationship is set as the core controlled variable in the subsequent sleep regulation process. All subsequent regulation actions revolve around changing this ratio to achieve the optimization goal, ensuring that the controlled variable is directly related to the node's energy consumption and operating efficiency.

[0070] The remaining battery power is collected in real time by the energy monitoring unit built into the sensor monitoring node. The remaining energy status is determined based on the actual remaining power as a percentage of the rated total power, and is divided into three states: sufficient energy, moderate energy, and insufficient energy. The number of data packets to be forwarded per unit time and the length of the data transmission queue are counted by the node's communication module. Combined with the node's routing forwarding capability, the routing load is determined, and is divided into three states: light load, medium load, and heavy load. The real-time remaining energy status information and routing load information are organized into a standardized feedback data format, which serves as the feedback input signal in the subsequent control and analysis process, ensuring that the feedback input signal can accurately reflect the node's operating resource status.

[0071] The sleep strategy library of the sensor monitoring node pre-stores sleep strategies corresponding to different ratios of activity and sleep durations, different remaining energy states, and different routing load combinations. Each strategy's impact on the overall network lifetime is clearly defined. With maximizing the overall network lifetime as the core optimization objective, for each acquired feedback input signal, all sleep strategies associated with that signal are extracted from the sleep strategy library. Pareto analysis is performed on these strategies, comparing the relationship between node energy consumption and overall network lifetime under different strategies. Unfavorable strategies with higher energy consumption but shorter network lifetimes are eliminated, while advantageous strategies that cannot be simultaneously surpassed by other strategies in terms of energy consumption control and network lifetime extension are retained. All retained advantageous strategies are integrated to form an optimized sleep strategy set for the sensor monitoring node.

[0072] Based on the remaining energy status and routing load corresponding to the current feedback input signal, the target sleep strategy that best suits the current node's operating state is selected from the optimized sleep strategy set. This target strategy clarifies the optimal ratio between activity time and sleep time. Based on this optimal ratio and combined with the node's hardware operating characteristics, specific activity time intervals and sleep time intervals are determined. It is clarified which functional modules of the node must remain running and which functional modules can be started as needed during the activity time. At the same time, hardware shutdown rules for the node during the sleep time are set, including the type of modules to be shut down, shutdown sequence, wake-up conditions, etc. These specific time intervals, functional operation rules, and hardware control requirements are integrated into specific parameters that can be directly configured, resulting in the sleep energy consumption control parameters for the sensor monitoring node.

[0073] According to the hardware instruction parsing protocol of the sensor monitoring node, information such as the activity duration range, sleep duration range, functional operation rules, and hardware control requirements in the sleep energy consumption control parameters are arranged in a fixed order. Each parameter is assigned a unique identification code to ensure that the node can accurately identify the specific meaning of each parameter. The arranged parameters and identification codes are converted into binary code that can be directly read by the node hardware. A verification field is added during the encoding process. The verification field is generated by performing logical operations on the core parameter information and is used to verify the integrity and accuracy of the information when the node receives instructions, so as to avoid data loss or tampering during transmission. Finally, a complete sleep control instruction for the sensor monitoring node is formed.

[0074] The beneficial effect is that, with the core objective of maximizing the overall network lifetime, the ratio of node activity to sleep time is clearly defined as the controlled variable. Combined with the remaining energy status and routing load that truly reflect the node status as feedback input, Pareto analysis is used to screen advantageous strategies to form an optimized sleep strategy set. Based on the strategy set, parameterized configuration tailored to node characteristics is completed to obtain sleep energy consumption control parameters. After standardized encapsulation, encoding and verification processing, reliable sleep control instructions are generated, effectively balancing node energy consumption and operational needs, and extending the overall network lifetime while ensuring the efficient and stable operation of nodes.

[0075] The operation performance feedback and parameter tuning module 106 is used to monitor the execution status and energy consumption data of the instruction control strategy and the sleep regulation instruction, so as to obtain the operation performance evaluation report of the sensor monitoring node, and to synchronously regulate the low power operation control parameters based on the operation performance evaluation report.

[0076] In this embodiment of the invention, when the operation performance feedback and parameter tuning module executes the monitoring of the command control strategy and the sleep regulation command's execution status and energy consumption data to obtain the operation performance evaluation report of the sensor monitoring node, and synchronously regulates the low-power operation control parameters based on the operation performance evaluation report, it is specifically used for: The response delay data and execution success rate data generated during the actual execution of the command control strategy are used as the execution status dataset of the sensing and monitoring node; The node activation duration data and sleep-wake interval data corresponding to the hibernation control command within the execution cycle are used as the energy consumption monitoring dataset of the sensing monitoring node. The performance of the execution status dataset and the energy consumption monitoring dataset is predicted to obtain the operational performance evaluation report of the sensor monitoring node. Based on the operational performance evaluation report, the low-power operation control parameters are adaptively adjusted to complete the synchronous control of the sensing and monitoring nodes.

[0077] When the operational performance feedback and parameter optimization module performs adaptive adjustments to the low-power operation control parameters based on the operational performance evaluation report to complete the synchronous control of the sensing and monitoring nodes, it is specifically used for: When the operational performance evaluation report shows that the system energy consumption is too high and the operational performance is good, the sampling frequency benchmark value of the sensor monitoring node and the sleep duration benchmark value shall be appropriately reduced without reducing the operational performance. If the performance evaluation report shows that the system performance is insufficient but the energy consumption is low, the baseline value of the acquisition frequency of the sensor monitoring node should be appropriately increased and the baseline value of the sleep time should be shortened. If the operational performance evaluation report shows a decrease in network connectivity, then the baseline value of the transmit power of the key nodes in the sensing and monitoring nodes shall be increased. The adjusted acquisition frequency reference value and the sleep duration reference value will be used as the basis for initializing the parameters in the next cycle.

[0078] The system monitors the entire process of command control strategies from issuance to sensor monitoring nodes to the completion of corresponding actions. The system accurately records the issuance and completion times of each command using a built-in time recording module, calculating the time difference between these two times. This difference represents the response latency data for a single command. All command response latency data is categorized and organized by command type. Simultaneously, the system receives the execution results of each command from the sensor monitoring nodes, clearly distinguishing between successful and failed executions. It then calculates the percentage of successfully executed commands out of the total number of commands issued. Finally, the system integrates the categorized response latency data with the statistically obtained execution success rate data to form the execution status dataset of the sensor monitoring nodes.

[0079] Using a complete execution cycle of the hibernation control command as the statistical unit, the node's status monitoring module records the start and stop times of the node entering the active state after waking up from the hibernation state within that cycle. The duration of each active phase is calculated and accumulated to obtain the node activation duration data within that execution cycle. At the same time, the start time of each hibernation and the start time of the next wake-up are recorded within that cycle, and the time interval between two adjacent wake-ups is calculated. All hibernation-wake-up intervals are recorded sequentially in chronological order. The statistically obtained node activation duration data is combined with the sequentially organized hibernation-wake-up interval data to form the energy consumption monitoring dataset of the sensor monitoring node.

[0080] Historical performance data from sensor monitoring nodes under similar operating scenarios is retrieved. The current execution status dataset is compared and analyzed with the energy consumption monitoring dataset and historical performance data to determine whether the current response latency data is within a reasonable historical range. If the response latency exceeds the reasonable range, it is marked as a performance bottleneck. The difference between the execution success rate data and the historical success probability is analyzed to identify the current strengths or weaknesses at the execution level. The node activation duration data is evaluated to determine whether it exceeds the activation duration range corresponding to the historical optimal energy consumption, and whether the sleep-wake interval is consistent with the interval pattern of historical high-efficiency operation cycles. Based on these comparative analysis results, the performance trend of the node's subsequent operation is predicted, the current performance level, existing core problems, and potential impacts are identified, and this information is organized according to the structure of "performance status - problem analysis - trend prediction" to form an operational performance evaluation report for the sensor monitoring node.

[0081] To address the performance shortcomings identified in the operational performance evaluation report, corresponding adjustments were made to the low-power operation control parameters: If the report indicates excessive response latency, and the analysis suggests that the excessively high data acquisition frequency is causing excessive processing pressure on the nodes, the initial data acquisition frequency was reduced to decrease the amount of data processed by the nodes in real time. If the report indicates excessive energy consumption, and the core reason is insufficient sleep duration, the initial sleep duration was extended to reduce the number of node activations. If the report indicates low execution success rate, and this is related to insufficient transmission power leading to communication instability, the initial transmission power level was increased to enhance communication reliability. During the adjustment process, it was ensured that each parameter modification directly corresponds to the issues identified in the evaluation report. After modification, the adaptability of the parameters to the node hardware characteristics and operating scenarios was verified to ensure that the adjusted low-power operation control parameters can specifically address the current performance issues and achieve synchronous control of the sensor monitoring nodes.

[0082] Carefully review the energy consumption monitoring data and performance assessment conclusions in the operational performance evaluation report. When the report clearly shows that the system's current energy consumption is in a historically high range, and all operational performance indicators are within the preset good range, initiate the low-power optimization and adjustment process. Referencing the lowest acquisition frequency and longest sleep duration records of sensor monitoring nodes under the same operational scenarios with good performance, without changing the data acquisition accuracy or instruction execution logic, appropriately lower the baseline value of the acquisition frequency within a reasonable range of the historical best range to reduce the number of data acquisitions per unit time. At the same time, appropriately extend the baseline value of the sleep duration within the historical longest reasonable range to increase the duration of non-essential node function shutdown. During the adjustment process, monitor the node's operational performance indicators in real time to ensure that all performance indicators remain within the good range after the adjustment, and that no performance decline occurs.

[0083] A thorough analysis of the performance shortcomings and energy consumption data in the operational performance evaluation report is conducted. When the report indicates insufficient system performance, and energy consumption data shows a slow decline in remaining node power and a low percentage of active time, indicating a low energy consumption level, the performance improvement and adjustment process is initiated. Combining the node's hardware processing capabilities and data transmission requirements, and referencing the historical performance-compliant data collection frequency range, the baseline value of the data collection frequency is appropriately increased within a reasonable range to increase the number of data collections per unit time, ensuring more timely and comprehensive data acquisition. Simultaneously, referring to the historical performance-compliant sleep duration range, the baseline value of the sleep duration is appropriately shortened within that range to reduce the node's sleep duration, allowing the node to be in an active state more frequently to respond to commands and transmit data. After adjustments, performance changes are continuously tracked to ensure that energy consumption does not exceed the node's carrying capacity while gradually improving operational performance to the compliant range.

[0084] A comprehensive review of the network communication assessment content in the operational performance evaluation report was conducted. When the report clearly indicated a decline in network connectivity, the first step was to identify key nodes in the sensor monitoring network. These nodes include core routing nodes responsible for data aggregation and forwarding, core acquisition nodes covering key monitoring areas, and gateway nodes connecting different subnets. Based on the communication coverage requirements and hardware power limits of these key nodes, the baseline transmit power of these nodes was appropriately increased to enhance signal transmission strength and coverage, reducing communication interruptions or packet loss caused by weak signals. During the adjustment process, the transmit power of non-critical nodes was not increased simultaneously to ensure that the network connectivity issue was addressed specifically and that unnecessary energy waste was avoided.

[0085] The new acquisition frequency baseline value and new sleep duration baseline value determined through the above adjustment process are standardized and recorded, clearly indicating the adjustment time, adjustment basis, and applicable scenarios. These standardized adjusted parameters are written to the parameter configuration storage unit of the sensor monitoring node, overwriting the original initial parameter storage information. Simultaneously, a parameter adjustment log is established to fully record the parameter change history. When the sensor monitoring node starts up for the next operating cycle, the system automatically reads the adjusted acquisition frequency baseline value and sleep duration baseline value recorded in the parameter configuration storage unit. These are used as the initial parameters for the node's operation in the next cycle, completing the parameter initialization process and ensuring that the adjusted parameters continue to function, achieving dynamic optimization of the node's operating status.

[0086] The beneficial effects include comprehensively collecting response latency and execution success rate data of command control strategies, accurately recording node activation duration and sleep-wake interval information corresponding to sleep control commands, generating an operational performance evaluation report covering the current status, problems and trends through comparative analysis with historical performance data, and adjusting low-power operation control parameters based on the report to effectively solve performance shortcomings such as excessive response latency and excessive energy consumption, achieving dynamic adaptation of node operation performance and low power consumption requirements, and ensuring stable, efficient and long-term operation of sensor monitoring nodes.

[0087] Based on the different scenarios reflected in the operational performance evaluation report, the baseline values ​​of the acquisition frequency, sleep duration, and key node transmit power are adjusted in a targeted manner. When energy consumption is too high but performance is good, the low-power performance is optimized; when performance is insufficient but energy consumption is low, the operational performance is improved; and when network connectivity declines, communication stability is enhanced. The adjusted parameters are used as the basis for initialization in the next cycle to achieve dynamic adaptation between low-power operation control parameters and node operating status, taking into account energy consumption control, performance improvement, and network stability, and ensuring the continuous and efficient operation of the sensor monitoring nodes.

[0088] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0089] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A low-power management system for wireless detection of goaf areas, characterized in that, The system includes a parameter initialization module, a data integration module, a security assessment module, a policy response module, a node sleep control module, and an operational performance feedback and parameter optimization module, wherein: The parameter initialization module is used to initialize the parameters of the sensing and monitoring nodes in the target area to obtain the low-power operation control parameters of the target area. The data integration module is used to integrate the carbon monoxide concentration, temperature, ethylene concentration and acetylene concentration collected in the target area into multi-parameter environmental data of the target area based on the low-power operation control parameters. The safety assessment module is used to assess the safety status of the target area based on the comprehensive factor change rate of the multi-parameter environmental data and the data control log in the sensor monitoring node, and obtain the safety assessment result of the target area. The strategy response module is used to respond to the sensing and monitoring node based on the adaptive data control strategy of the target area and the security assessment result, so as to obtain the instruction control strategy of the sensing and monitoring node. The node sleep control module is used to optimize the overall network lifetime of the sensor monitoring node by taking the maximum network lifetime as the optimization goal, and in combination with the resource status of the sensor monitoring node, to adaptively control the activity time and sleep time of the sensor monitoring node, so as to obtain the sleep control command of the sensor monitoring node. The operation performance feedback and parameter tuning module is used to monitor the execution status and energy consumption data of the command control strategy and the sleep regulation command, so as to obtain the operation performance evaluation report of the sensor monitoring node, and to synchronously regulate the low power operation control parameters based on the operation performance evaluation report.

2. The low-power management system for wireless detection of goaf areas as described in claim 1, characterized in that, When the parameter initialization module initializes the parameters of the sensing and monitoring nodes in the target area to obtain the low-power operation control parameters of the target area, it is specifically used for: Based on the type of sensing and monitoring equipment in the target area and the deployment location of the sensing and monitoring nodes in the target area, assign a corresponding initial data acquisition frequency to the sensing and monitoring nodes; Based on the power supply method of the sensor monitoring node, set the initial sleep duration for the sensor monitoring node; Based on the network monitoring topology of the target area and the relay role of the sensor monitoring node, an initial transmission power level is set for the sensor monitoring node; The initial data acquisition frequency, the initial sleep duration, and the initial transmit power level are integrated into the low-power operation control parameters of the sensing and monitoring node.

3. The low-power management system for wireless detection of goaf areas as described in claim 1, characterized in that, When the data integration module integrates the carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration collected in the target area into multi-parameter environmental data of the target area based on the low-power operation control parameters, it is specifically used for: Based on the data acquisition frequency in the low-power operation control parameters, the carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration of the target area are obtained as the original data sequence of the target area; The original data sequence is standardized in terms of dimensions to obtain standardized parameter data for the target region. The standardized parameter data is combined and encapsulated to obtain a structured data packet for the target region; Based on the region identifier and acquisition time information of the target region, the structured data packet is spliced ​​to obtain multi-parameter environmental data of the target region.

4. The low-power management system for wireless detection of goaf areas as described in claim 1, characterized in that, When the safety assessment module performs a risk threshold assessment of the safety status of the target area based on the comprehensive factor change rate of the multi-parameter environmental data and the data control logs in the sensor monitoring nodes, and obtains the safety assessment result of the target area, it is specifically used for: Extract the time-series data of carbon monoxide concentration, temperature, ethylene concentration, and acetylene concentration from the multi-parameter environmental data; The average and instantaneous rates of change of the carbon monoxide concentration time series data, and the heating rate of the temperature time series data, are characterized as dynamic change indicators of the multi-parameter environmental data. Retrieve the corresponding historical control command records in the data control log and the execution response status records of the historical commands in the sensor monitoring node; Based on the dynamic change indicators, the historical control command records, the execution response status records, and the ethylene concentration data and acetylene concentration data as Boolean identifiers for the appearance of characteristic gases, a comprehensive evaluation matrix of the multi-parameter environmental data is constructed. Based on the historical impact rule base of the target area, the comprehensive evaluation matrix is ​​correlated and mapped to obtain the security evaluation result of the target area.

5. A low-power management system for wireless detection of goaf areas as described in claim 4, characterized in that, The calculation formula for the dynamic change index is as follows: ； In the formula, The aforementioned dynamic change index, The average rate of change in carbon monoxide concentration is the weighting factor. The average rate of change of carbon monoxide concentration in the time-series data of carbon monoxide concentration is given. The instantaneous rate of change of the carbon monoxide concentration is the weighting coefficient. The instantaneous rate of change of the carbon monoxide concentration is given. The preset instantaneous temperature weighting coefficient, The temperature rise rate of the temperature time series data. It is a very small positive number.

6. A low-power management system for wireless detection of goaf areas as described in claim 5, characterized in that, When the safety assessment module constructs a comprehensive assessment matrix of the multi-parameter environmental data based on the dynamic change indicators, the historical control command records, the execution response status records, and the Boolean identifiers of the ethylene and acetylene concentration data as characteristic gases, it is specifically used for: Tensor synthesis is performed on the dynamic change indicators to obtain the environmental parameter vector of the target area; The instruction types in the historical control instruction record are correlated and quantified with the status feedback in the execution response status record to obtain the historical control vector of the target area. Based on the characteristic gas concentration threshold of the target area, the ethylene concentration data and acetylene concentration data are compared by threshold, and the Boolean identifiers of the ethylene concentration data and acetylene concentration data are determined according to the comparison results. The environmental parameter vector, the historical control vector, and the Boolean identifier are reconstructed to obtain the comprehensive evaluation matrix of the target area.

7. A low-power management system for wireless detection of goaf areas as described in claim 1, characterized in that, When the policy response module executes the adaptive data control policy based on the target area and the security assessment result to respond to the sensing and monitoring node and obtain the instruction control policy of the sensing and monitoring node, it is specifically used for: Based on the adaptive data control strategy of the target area, the security assessment results are mapped and corrected to obtain the basic response strategy framework of the sensing and monitoring node. Combining the security assessment results with the real-time resource status of the sensing and monitoring node, the action parameters in the basic response strategy framework are instantiated and assigned values ​​to obtain the preliminary control instruction set of the sensing and monitoring node. The logical consistency and resource conflicts of the preliminary control instruction set are checked and detected, and the checked instruction set is converted into the instruction control strategy of the sensing and monitoring node.

8. A low-power management system for wireless detection of goaf areas as described in claim 1, characterized in that, When the node sleep control module executes an optimization objective of maximizing the overall network lifetime of the sensor monitoring nodes and, in conjunction with the resource status of the sensor monitoring nodes, adaptively controls the activity duration and sleep duration of the sensor monitoring nodes to obtain sleep control instructions for the sensor monitoring nodes, it is specifically used for: The ratio between the active duration and the sleep duration of the sensor monitoring node is used as the controlled variable. The remaining energy status and routing load of the sensing and monitoring nodes are used as feedback input signals; With the goal of maximizing the overall network lifetime of the sensor monitoring node, Pareto analysis is performed on the sleep strategy library of the sensor monitoring node based on the feedback input signal and the controlled variable to obtain the optimized sleep strategy set of the sensor monitoring node. Based on the optimized sleep strategy set, the sensor monitoring node is parameterized to obtain the sleep energy consumption control parameters of the sensor monitoring node. The sleep energy consumption control parameters are encapsulated and encoded to obtain the sleep control command of the sensing and monitoring node.

9. A low-power management system for wireless detection of goaf areas as described in claim 1, characterized in that, The operational performance feedback and parameter tuning module, when executing the monitoring of the command control strategy and the sleep regulation command's execution status and energy consumption data to obtain the operational performance evaluation report of the sensor monitoring node, and based on the operational performance evaluation report, synchronously regulates the low-power operation control parameters, specifically is used for: The response delay data and execution success rate data generated during the actual execution of the command control strategy are used as the execution status dataset of the sensing and monitoring node; The node activation duration data and sleep-wake interval data corresponding to the hibernation control command within the execution cycle are used as the energy consumption monitoring dataset of the sensing monitoring node. The performance of the execution status dataset and the energy consumption monitoring dataset is predicted to obtain the operational performance evaluation report of the sensor monitoring node. Based on the operational performance evaluation report, the low-power operation control parameters are adaptively adjusted to complete the synchronous control of the sensing and monitoring nodes.

10. A low-power management system for wireless detection of goaf areas as described in claim 9, characterized in that, When the operational performance feedback and parameter optimization module performs adaptive adjustments to the low-power operation control parameters based on the operational performance evaluation report to complete the synchronous control of the sensing and monitoring nodes, it is specifically used for: When the operational performance evaluation report shows that the system energy consumption is too high and the operational performance is good, the sampling frequency benchmark value of the sensor monitoring node and the sleep duration benchmark value shall be appropriately reduced without reducing the operational performance. If the performance evaluation report shows that the system performance is insufficient but the energy consumption is low, the baseline value of the acquisition frequency of the sensor monitoring node should be appropriately increased and the baseline value of the sleep time should be shortened. If the operational performance evaluation report shows a decrease in network connectivity, then the baseline value of the transmit power of the key nodes in the sensing and monitoring nodes shall be increased. The adjusted acquisition frequency reference value and the sleep duration reference value will be used as the basis for initializing the parameters in the next cycle.

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