Energy-saving fire safety early warning system based on Internet platform data sharing

Abstract

The invention discloses an energy-saving fire safety early warning system based on Internet platform data sharing, and relates to the technical field of fire safety, and the technical scheme is characterized by comprising the following steps: obtaining historical energy efficiency data and safety event data of a fire fighting system in a target area, acquiring real-time fire-fighting energy efficiency monitoring data and dynamic safety sensing data of the target area; obtaining a global fire safety anomaly coefficient corresponding to the target area according to the hierarchical energy efficiency anomaly coefficient and the safety priority weight; obtaining a global energy-saving early warning mode corresponding to the target area according to the energy efficiency association type early warning evaluation interval and the global fire safety anomaly coefficient; and outputting an energy-saving fire safety early warning result of the target area according to the energy efficiency optimization early warning mode, the hierarchical energy-saving safety early warning mode and the global energy-saving early warning mode. The method has the effect of improving the accuracy and timeliness of fire early warning.

Description

Technical Field

[0001] This invention relates to the field of fire safety technology, and more specifically, to an energy-saving fire safety early warning system based on data sharing on an internet platform. Background Technology

[0002] Traditional fire early warning systems often prioritize safety as a single objective, lacking a systematic consideration of energy efficiency. Many fire-fighting equipment operate with high redundancy for extended periods, such as excessively high standby power of emergency pumps and a lack of coordinated energy consumption optimization when multiple systems are linked. This results in significant daily energy redundancy, increasing maintenance costs and failing to meet the demands of green and low-carbon development. Existing fire early warning systems rely heavily on single-dimensional real-time data, lacking in-depth integration and hierarchical reference to historical data. For example, they rely solely on real-time pressure values ​​to determine equipment status without establishing benchmarks based on historical energy efficiency curves and safety event patterns. This leads to insufficient accuracy in early warnings, often resulting in over-warning or delayed warnings. Furthermore, they fail to achieve hierarchical risk management from equipment and systems to the entire domain.

[0003] Furthermore, as the complexity of fire protection systems in large buildings, parks, and other scenarios increases, the traditional decentralized fire protection data management model makes it difficult to achieve cross-module data sharing and collaborative analysis. This results in a lack of a global perspective on the operational status of the fire protection system, making it difficult to identify potential energy efficiency anomalies in advance, and also making it impossible to quickly locate the problem level and match the corresponding response plan when anomalies occur. Summary of the Invention

[0004] In view of the shortcomings of existing technologies, the purpose of this invention is to provide an energy-saving fire safety early warning system based on data sharing on an Internet platform.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An energy-saving fire safety early warning system based on data sharing on an internet platform includes: Acquisition module: Acquires historical energy efficiency data and safety event data of the fire protection system in the target area, and acquires real-time fire energy efficiency monitoring data and dynamic safety perception data of the target area; Generation module: Obtains benchmark fire safety energy efficiency standards through historical energy efficiency data and safety event data, generates energy efficiency standard nodes based on benchmark fire safety energy efficiency standards, and generates fire safety energy efficiency levels based on energy efficiency standard nodes; The first processing module processes real-time fire energy efficiency monitoring data, dynamic safety perception data, fire safety energy efficiency levels, and energy efficiency standard nodes to obtain an energy efficiency optimization early warning method. The second processing module: obtains the hierarchical energy efficiency anomaly coefficient corresponding to the fire safety energy efficiency level based on the energy efficiency loss anomaly coefficient; obtains the risk energy efficiency early warning level and the hierarchical energy-saving safety early warning method based on the energy efficiency correlation early warning assessment interval and the hierarchical energy efficiency anomaly coefficient. The third processing module: obtains the overall fire safety anomaly coefficient of the target area based on the hierarchical energy efficiency anomaly coefficient and the safety priority weight; and obtains the overall energy-saving early warning method of the target area based on the energy efficiency correlation early warning assessment interval and the overall fire safety anomaly coefficient. Output module: Outputs energy-saving fire safety early warning results for the target area based on energy efficiency optimization early warning method, hierarchical energy-saving safety early warning method, and whole-area energy-saving early warning method.

[0006] Preferably, the baseline fire safety energy efficiency standard is obtained by combining historical energy efficiency data and safety incident data, specifically including the following steps: Set a benchmark energy efficiency type, wherein the benchmark energy efficiency type includes equipment low-consumption safety type, system collaborative energy efficiency type, emergency response energy efficiency type and normal operation benchmark type; Based on the benchmark energy efficiency type, historical energy efficiency data and safety incident data of the fire protection system are extracted to obtain the benchmark fire safety energy efficiency standard; The benchmark fire safety energy efficiency standards include low-consumption safety benchmarks for equipment, system collaborative energy efficiency benchmarks, emergency response energy efficiency benchmarks, and routine operation energy efficiency benchmarks.

[0007] Preferably, the fire safety energy efficiency levels include equipment low-consumption safety levels, system collaborative energy efficiency levels, emergency response energy efficiency levels, and normal operation energy efficiency levels.

[0008] Preferably, the energy efficiency optimization and early warning method is obtained by processing real-time fire energy efficiency monitoring data, dynamic safety perception data, fire safety energy efficiency levels, and energy efficiency standard nodes, specifically including the following steps: The real-time fire safety operation status of the target area is obtained by analyzing real-time fire energy efficiency monitoring data and dynamic safety perception data. The target energy efficiency level is obtained by analyzing the real-time fire safety operation status and the fire safety energy efficiency level; the target fire safety energy efficiency standard is obtained by analyzing the real-time fire safety operation status and the energy efficiency standard node. The abnormal fire safety operation status and the abnormal coefficient of energy efficiency loss are obtained by judging the real-time fire safety operation status and the target fire safety energy efficiency standard. An energy efficiency optimization early warning method is derived based on the energy efficiency correlation early warning assessment interval and the energy efficiency loss anomaly coefficient.

[0009] Preferably, the real-time fire safety operation status of the target area is obtained by analyzing real-time fire energy efficiency monitoring data and dynamic safety perception data, specifically including the following steps: Based on the benchmark energy efficiency type, collaborative feature extraction is performed on real-time fire energy efficiency monitoring data and dynamic safety perception data to obtain the real-time fire safety operation status and operation status type. The real-time fire safety operation status includes equipment energy efficiency operation status, system collaborative operation status, emergency response preparedness status, and normal safe operation status.

[0010] Preferably, the target energy efficiency level is obtained by analyzing the real-time fire safety operation status and the fire safety energy efficiency level; the target fire safety energy efficiency standard is obtained by analyzing the real-time fire safety operation status and the energy efficiency standard node. Specifically, this includes the following steps: The operation status type corresponding to the real-time fire safety operation status is matched with the benchmark energy efficiency type corresponding to the fire safety energy efficiency level. The fire safety energy efficiency level that matches the baseline energy efficiency type and the operating status type is marked as the target energy efficiency level corresponding to the real-time fire safety operating status. The real-time fire safety operation status is compared with the benchmark fire safety energy efficiency standards corresponding to the level nodes in the target energy efficiency level. The nodes in the target energy efficiency level that match the characteristics of real-time fire safety operation status are marked as energy efficiency benchmark nodes, and the benchmark fire safety energy efficiency standards corresponding to the energy efficiency benchmark nodes are marked as target fire safety energy efficiency standards.

[0011] Preferably, the energy efficiency-related early warning assessment interval includes a behavioral energy efficiency early warning interval, a hierarchical energy efficiency early warning interval, and a global energy efficiency early warning interval; There are corresponding energy efficiency optimization warning response plans for each behavioral energy efficiency warning range. Each energy efficiency warning range corresponds to a corresponding energy-saving safety warning response plan. The energy efficiency warning range for the entire region corresponds to a corresponding energy conservation warning response plan for the entire region.

[0012] Preferably, the abnormal fire safety operating status and energy efficiency loss anomaly coefficient are determined by judging the real-time fire safety operating status and the target fire safety energy efficiency standard, specifically including the following steps: Compare the real-time fire safety operation status with the target fire safety energy efficiency standards to determine their feature fit. If the real-time fire safety operation status is less than the set threshold in terms of the characteristic conformity with the target fire safety energy efficiency standard, then the real-time fire safety operation status will be marked as an abnormal fire safety operation status. Obtain the energy efficiency deviation value and safety deviation value between the abnormal fire safety operation status and the target fire safety energy efficiency standard, and obtain the comprehensive abnormal deviation value based on the energy efficiency deviation value and safety deviation value; Based on the operation status type of the abnormal fire safety operation status, set the first energy efficiency weight of the abnormal fire safety operation status; obtain the energy efficiency redundancy ratio corresponding to the abnormal fire safety operation status, and set the second energy efficiency weight of the abnormal fire safety operation status based on the energy efficiency redundancy ratio; The energy efficiency loss weights for abnormal fire safety operation states are obtained based on the first and second energy efficiency weights. The energy efficiency loss anomaly coefficient is obtained based on the energy efficiency loss weight and the comprehensive anomaly deviation value.

[0013] Preferably, the energy efficiency optimization early warning method is obtained based on the energy efficiency correlation early warning assessment interval and the energy efficiency loss anomaly coefficient, specifically including the following steps: The coefficients of abnormal energy efficiency loss corresponding to abnormal fire safety operation status are matched with the behavioral energy efficiency early warning interval. The energy efficiency optimization early warning response scheme corresponding to the energy efficiency early warning interval to which the energy efficiency loss anomaly coefficient belongs is marked as the energy efficiency optimization early warning method.

[0014] Preferably, the overall fire safety anomaly coefficient corresponding to the target area is obtained based on the hierarchical energy efficiency anomaly coefficient and the safety priority weight; the overall energy-saving early warning method corresponding to the target area is obtained based on the energy efficiency correlation early warning assessment interval and the overall fire safety anomaly coefficient, specifically including the following steps: Set the safety priority weight of the fire safety energy efficiency level based on the benchmark energy efficiency type corresponding to the fire safety energy efficiency level; The overall contribution anomaly coefficient is obtained by weighting the hierarchical energy efficiency anomaly coefficients according to the safety priority weights. The total fire safety anomaly coefficient of the target area is obtained by summing the total contribution anomaly coefficients of the fire safety energy efficiency levels. Compare the overall fire safety anomaly coefficient of the target area with the overall energy efficiency early warning interval; If the overall fire safety anomaly coefficient corresponding to the target area is not within the overall energy efficiency warning range, then there is no need to issue an overall fire safety warning for the target area; if the overall fire safety anomaly coefficient corresponding to the target area is within the overall energy efficiency warning range, then there is a need to issue an overall fire safety warning for the target area. The energy-saving early warning response scheme corresponding to the energy efficiency early warning interval of the entire region to which the abnormal coefficient of the entire region fire safety belongs is marked as the energy-saving early warning method of the entire region.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention integrates historical energy efficiency data, safety event data, and real-time monitoring data through an acquisition module. This provides a comprehensive information foundation for subsequent analysis and, through a generation module, establishes benchmark fire safety energy efficiency standards and energy efficiency levels, providing quantifiable references for fire protection system operation. It can clearly define energy-saving directions from equipment, system, emergency, and routine dimensions while ensuring safety, effectively avoiding energy waste caused by blind operation of the fire protection system. The processing module outputs energy efficiency optimization early warnings based on real-time operating status, promptly correcting energy efficiency anomalies in individual devices or localized components, preventing small anomalies from escalating into major risks. The second processing module focuses on anomaly assessment at the energy efficiency level, outputting matching tiered energy-saving safety early warning schemes for different levels of issues such as low equipment consumption and system coordination. This ensures independent optimization at each level while preventing the transmission of anomalies between levels. The third processing module integrates anomalies across the entire domain through safety priority weighting, outputting a comprehensive energy-saving early warning method that coordinates the overall fire protection energy efficiency of the region, ensuring the system's safety response capability in emergency scenarios while also considering energy consumption control during daily operation. The output module integrates the three types of early warning methods to form the final result, providing management with clear and tiered handling guidance. Improving the accuracy and timeliness of fire early warning can also continuously optimize the energy consumption level of the fire protection system, achieving a long-term balance between safety assurance and energy-saving benefits, and is especially suitable for complex fire protection systems in large parks, commercial complexes and other scenarios. Attached Figure Description

[0016] Figure 1 This invention provides a schematic diagram of an energy-saving fire safety early warning system based on data sharing on an internet platform, as an embodiment of the present invention. Figure 2 This invention provides a schematic diagram illustrating the steps involved in obtaining an energy efficiency optimization early warning method within an energy-saving fire safety early warning system based on data sharing on an internet platform, as described in an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0020] Reference Figures 1-2 As shown.

[0021] The embodiments further illustrate the energy-saving fire safety early warning system based on data sharing on an Internet platform proposed in this invention.

[0022] An energy-saving fire safety early warning system based on data sharing on an internet platform includes: Acquisition module: Acquires historical energy efficiency data and safety event data of the fire protection system in the target area, and acquires real-time fire energy efficiency monitoring data and dynamic safety perception data of the target area; Generation module: Obtains benchmark fire safety energy efficiency standards through historical energy efficiency data and safety event data, generates energy efficiency standard nodes based on benchmark fire safety energy efficiency standards, and generates fire safety energy efficiency levels based on energy efficiency standard nodes; The first processing module processes real-time fire energy efficiency monitoring data, dynamic safety perception data, fire safety energy efficiency levels, and energy efficiency standard nodes to obtain an energy efficiency optimization early warning method. The second processing module: obtains the hierarchical energy efficiency anomaly coefficient corresponding to the fire safety energy efficiency level based on the energy efficiency loss anomaly coefficient; obtains the risk energy efficiency early warning level and the hierarchical energy-saving safety early warning method based on the energy efficiency correlation early warning assessment interval and the hierarchical energy efficiency anomaly coefficient. Based on the energy efficiency loss anomaly coefficient corresponding to different fire safety energy efficiency levels, including equipment low-consumption safety level, system collaborative energy efficiency level, emergency response energy efficiency level, and normal operation energy efficiency level, the corresponding energy efficiency anomaly coefficient for each level is determined. A single energy efficiency loss anomaly coefficient is matched to its specific energy efficiency level dimension, ensuring that anomalies at each level can be quantified independently.

[0023] The system retrieves the hierarchical energy efficiency warning intervals from the energy efficiency-related early warning assessment intervals and compares the hierarchical energy efficiency anomaly coefficients with these intervals. The hierarchical energy efficiency warning intervals are pre-defined numerical ranges corresponding to different risk levels, such as low-risk, medium-risk, and high-risk intervals. When a hierarchical energy efficiency anomaly coefficient falls within a certain energy efficiency-related early warning assessment interval, the corresponding risk energy efficiency warning level is determined. For example, if the hierarchical energy efficiency anomaly coefficient of a certain level is in the high-risk interval, then the risk energy efficiency warning level for that level is a high-risk level.

[0024] After determining the risk energy efficiency warning level, the corresponding energy-saving safety warning method for the corresponding energy efficiency warning interval is invoked. Taking the low-consumption safety level of the equipment as an example, if the level energy efficiency anomaly coefficient of this level is in the medium-risk range, the corresponding level energy-saving safety warning method is to adjust the operating power of the equipment to reduce energy loss while maintaining a safe state; if it is in the high-risk range, the warning method is to activate the temporary standby mode of the equipment and push a maintenance reminder.

[0025] The tiered energy efficiency anomaly coefficient = energy efficiency loss anomaly coefficient × tiered weight coefficient. The tiered weight coefficient is a value set according to the importance of different fire safety energy efficiency tiers. For example, the tiered weight coefficient for the emergency response energy efficiency tier is set to 1.2, and the tiered weight coefficient for the normal operation energy efficiency tier is set to 0.8, thus reflecting the different priorities of different tiers in the overall system. The energy efficiency loss anomaly coefficient is assigned a weight based on the tiered dimension, enabling the tiered energy efficiency anomaly coefficient to reflect the actual degree of anomaly at the corresponding tier.

[0026] The third processing module: obtains the overall fire safety anomaly coefficient of the target area based on the hierarchical energy efficiency anomaly coefficient and the safety priority weight; and obtains the overall energy-saving early warning method of the target area based on the energy efficiency correlation early warning assessment interval and the overall fire safety anomaly coefficient. Output module: Outputs energy-saving fire safety early warning results for the target area based on energy efficiency optimization early warning method, hierarchical energy-saving safety early warning method, and whole-area energy-saving early warning method.

[0027] The baseline fire safety energy efficiency standard is obtained by combining historical energy efficiency data and safety incident data, specifically including the following steps: Set benchmark energy efficiency types, which include equipment low-consumption safety type, system collaborative energy efficiency type, emergency response energy efficiency type, and normal operation benchmark type; Based on the benchmark energy efficiency type, historical energy efficiency data and safety incident data of the fire protection system are extracted to obtain the benchmark fire safety energy efficiency standard; Among them, the benchmark fire safety energy efficiency standards include low-consumption safety benchmarks for equipment, system collaborative energy efficiency benchmarks, emergency response energy efficiency benchmarks, and routine operation energy efficiency benchmarks.

[0028] The low-energy-consumption and safety type focuses on the low energy consumption and safe operation status of the fire-fighting equipment itself, such as the daily energy consumption and fault records of fire alarms and sprinkler systems; the system coordination energy efficiency type targets the coordination efficiency between multiple devices or subsystems, such as the linkage response time and energy consumption of the fire alarm system and the sprinkler system; the emergency response energy efficiency type focuses on the energy efficiency performance under sudden safety events, such as the starting speed and energy consumption intensity of fire pumps when a fire occurs; and the normal operation benchmark type corresponds to the basic energy efficiency level of the system under normal conditions, such as the average energy consumption of the fire-fighting system in standby mode all day.

[0029] Historical energy efficiency data and safety event data of the fire protection system are categorized and extracted. Taking the low-energy-consumption safety type of equipment as an example, energy consumption data of each fire protection device and safety event records related to equipment energy consumption are filtered from historical data. For example, the average daily energy consumption data of a sprinkler system for three consecutive months, as well as event information of the device malfunctioning due to excessive energy consumption. For the system collaborative energy efficiency type, energy consumption data and safety events of linkage failure when multiple systems are linked are extracted. For example, the response energy consumption of the sprinkler system after the alarm system is triggered, as well as event records of safety risks caused by the linkage delay between the two systems.

[0030] This classification and extraction process forms the corresponding benchmark fire safety energy efficiency standards. Specifically, the low-consumption safety benchmark for equipment is the historical data statistical value under the low-consumption safety type of the equipment. For example, the daily average energy consumption threshold of a certain model of fire alarm is set as the 95th percentile of its historical daily average energy consumption. The system coordination energy efficiency benchmark is the efficiency threshold of system coordination operation. For example, the benchmark for the linkage time between the alarm system and the sprinkler system can be set as the average of the historical linkage time. The emergency response energy efficiency benchmark is the upper limit of energy efficiency in emergency scenarios. For example, the benchmark for the energy consumption per unit time after the fire pump is started can be set as the 80th percentile of the historical energy consumption under emergency conditions. The normal operation energy efficiency benchmark is the energy consumption benchmark in daily standby state. For example, the benchmark for the total energy consumption of the fire protection system in 24-hour standby is set as the average of the historical normal energy consumption.

[0031] The equipment low-consumption safety benchmark = (sum of historical daily average energy consumption data of the equipment) ÷ number of historical operating days of the equipment × 0.95, where 0.95 is a coefficient based on safety redundancy settings. Each benchmark energy efficiency type corresponds to a specific benchmark standard, providing a reference for subsequent real-time data comparison and anomaly judgment.

[0032] Fire safety energy efficiency levels include low-consumption safety level of equipment, system coordination energy efficiency level, emergency response energy efficiency level, and normal operation energy efficiency level.

[0033] The low-energy consumption safety level of equipment corresponds to the low-energy consumption safety benchmark and is an energy efficiency level constructed around individual fire protection equipment. The low-energy consumption safety level is divided into different grade ranges based on the equipment's energy consumption and safety performance. For example, for fire sprinkler pumps, Level 1 is defined as daily average energy consumption below 2 kWh and no fault records; Level 2 is daily average energy consumption of 2 to 3 kWh and no more than 1 fault per month; and Level 3 is daily average energy consumption above 3 kWh or more than 2 faults per month. The threshold for the low-energy consumption safety level is calculated as: Low-energy consumption safety benchmark × (1 + level coefficient), where the level coefficient is 0.1 and 0.2 respectively, from lowest to highest. For example, when the low-energy consumption safety benchmark is 2 kWh, the Level 1 threshold is 2 × 1.1 = 2.2 kWh, and the Level 2 threshold is 2 × 1.2 = 2.4 kWh, thus clearly defining the boundaries of each level.

[0034] The system collaborative energy efficiency levels correspond to the system collaborative energy efficiency benchmark, focusing on the collaborative operational efficiency between multiple fire protection systems. Taking the collaboration between alarm systems and smoke exhaust systems as an example, the system collaborative energy efficiency levels are divided into grades based on linkage response time and collaborative energy consumption. For example, Level 1 is a linkage response time of less than 3 seconds and collaborative energy consumption of less than 5 kWh / time; Level 2 is a response time of 3 to 5 seconds and energy consumption of 5 to 8 kWh / time; and Level 3 is a response time of more than 5 seconds or energy consumption of more than 8 kWh / time. The system collaborative energy efficiency level score = (system collaborative energy efficiency benchmark - actual collaborative energy consumption) ÷ system collaborative energy efficiency benchmark × (benchmark response time ÷ actual response time). A score higher than 0.9 is Level 1, 0.7 to 0.9 is Level 2, and lower than 0.7 is Level 3, thus quantifying the level corresponding to collaborative performance.

[0035] Emergency response energy efficiency levels correspond to emergency response energy efficiency benchmarks, targeting energy efficiency and response capabilities in emergency scenarios. For example, in fire emergency scenarios, emergency response energy efficiency levels are divided into grades based on the startup time of emergency equipment and energy consumption during the emergency phase. Level 1 is startup time less than 2 seconds and energy consumption less than 10 kWh in 1 hour; Level 2 is startup time 2 to 4 seconds and energy consumption 10 to 15 kWh; and Level 3 is startup time exceeding 4 seconds or energy consumption exceeding 15 kWh. The core of these levels is to ensure the rationality of energy efficiency in emergency situations, therefore, hard thresholds are set: the minimum requirement for an emergency response energy efficiency level is that the actual startup time is ≤ the upper limit of startup time in the emergency response energy efficiency benchmark, and the actual energy consumption is ≤ the upper limit of energy consumption in the emergency response energy efficiency benchmark. If these requirements are not met, the level is directly assigned to the lowest level.

[0036] The normal operation energy efficiency level corresponds to the normal operation energy efficiency benchmark, covering the energy efficiency status of the fire protection system during daily standby and routine inspections. For example, in the daily standby state, the normal operation energy efficiency level is divided into levels based on the average daily standby energy consumption and equipment inspection energy consumption: Level 1 is an average daily standby energy consumption of less than 1 kWh and a single inspection energy consumption of less than 0.5 kWh; Level 2 is an average daily standby energy consumption of 1 to 1.5 kWh and an inspection energy consumption of 0.5 to 1 kWh; and Level 3 is an average daily standby energy consumption of more than 1.5 kWh or an inspection energy consumption of more than 1 kWh. The normal operation energy efficiency level coefficient = actual normal operation energy consumption ÷ normal operation energy efficiency benchmark. A normal operation energy efficiency level coefficient less than 0.9 is Level 1, a normal operation energy efficiency level coefficient between 0.9 and 1.1 is Level 2, and a normal operation energy efficiency level coefficient greater than 1.1 is Level 3, thus reflecting the degree of deviation in energy efficiency during daily operation.

[0037] The energy efficiency optimization and early warning method is obtained by processing real-time fire energy efficiency monitoring data, dynamic safety perception data, fire safety energy efficiency levels, and energy efficiency standard nodes. The specific steps include: The real-time fire safety operation status of the target area is obtained by analyzing real-time fire energy efficiency monitoring data and dynamic safety perception data. The target energy efficiency level is obtained by analyzing the real-time fire safety operation status and the fire safety energy efficiency level; the target fire safety energy efficiency standard is obtained by analyzing the real-time fire safety operation status and the energy efficiency standard node. The abnormal fire safety operation status and the abnormal coefficient of energy efficiency loss are obtained by judging the real-time fire safety operation status and the target fire safety energy efficiency standard. An energy efficiency optimization early warning method is derived based on the energy efficiency correlation early warning assessment interval and the energy efficiency loss anomaly coefficient.

[0038] The real-time fire safety operation status of the target area is determined by analyzing real-time fire energy efficiency monitoring data and dynamic safety perception data. Based on the baseline energy efficiency type, collaborative feature extraction is performed on the two types of data. For example, for the low-power safety type of equipment, real-time power data of the fire sprinkler pump is extracted as energy efficiency monitoring data, and the pump's pressure stability value is extracted as safety perception data. Combining these two types of data yields the corresponding real-time fire safety operation status of the equipment, specifically its energy efficiency operation status. Similarly, for the system collaborative energy efficiency type, the real-time linkage response time of the alarm system and smoke exhaust system is extracted as energy efficiency monitoring data, and the change in regional smoke concentration after linkage is extracted as safety perception data. Combining these yields the system collaborative operation status. This approach clarifies the actual operation of the current fire protection system from different dimensions.

[0039] The real-time fire safety operation status is determined by comparing it with the fire safety energy efficiency level to obtain the target energy efficiency level. The type of the real-time operation status is then matched with the type of the fire safety energy efficiency level. For example, if the real-time operation status is equipment energy efficiency operation status, the corresponding equipment low-power safety level is matched, and this level is the target energy efficiency level; if the real-time operation status is system collaborative operation status, the system collaborative energy efficiency level is matched as the target energy efficiency level. The real-time fire safety operation status is then compared with energy efficiency standard nodes to obtain the target fire safety energy efficiency standard: taking equipment energy efficiency operation status as an example, the characteristic data of this status is compared with the equipment low-power safety benchmark corresponding to each level node in the equipment low-power safety level. If the real-time power of this status has the highest degree of conformity with the benchmark power corresponding to a certain level node, the benchmark corresponding to that node is the target fire safety energy efficiency standard.

[0040] The real-time fire safety operation status is compared with the target fire safety energy efficiency standard to determine abnormal fire safety operation status and an abnormal energy efficiency loss coefficient. Specifically, the characteristic fit between the two is first calculated. If the fit is lower than a set threshold, the current operation status is determined to be abnormal. For example, if the baseline power of the equipment in the target fire safety energy efficiency standard is 2 kW, and the power in the real-time operation status is 3 kW, the fit is lower than the threshold, so the status is an abnormal fire safety operation status. The energy efficiency deviation value and the safety deviation value are obtained. Assuming the energy efficiency deviation value is the difference between the real-time power and the baseline power of 1 kW, and the safety deviation value is the difference between the real-time pressure stability value and the baseline stability value of 0.2 MPa, the comprehensive abnormal deviation value = energy efficiency deviation value × 0.6 + safety deviation value × 0.4, where 0.6 and 0.4 are the weights of the two types of deviations, and the comprehensive abnormal deviation value is 1 × 0.6 + 0.2 × 0.4 = 0.68. The first energy efficiency weight is set based on the operating status type, for example, the first energy efficiency weight for the equipment's operating status is 0.7; the second energy efficiency weight is set based on the energy efficiency redundancy ratio, if the energy efficiency redundancy ratio of the equipment is 20%, then the second energy efficiency weight is 0.3. Energy efficiency loss weight = first energy efficiency weight × 0.8 + second energy efficiency weight × 0.2, so the energy efficiency loss weight is 0.7 × 0.8 + 0.3 × 0.2 = 0.62. Energy efficiency loss anomaly coefficient = energy efficiency loss weight × comprehensive anomaly deviation value, substituting the data, we get the energy efficiency loss anomaly coefficient as 0.62 × 0.68 ≈ 0.4216.

[0041] The energy efficiency optimization early warning method is derived based on the energy efficiency correlation early warning assessment range and the energy efficiency loss anomaly coefficient. The energy efficiency loss anomaly coefficient is matched with the behavioral energy efficiency early warning range. If the coefficient falls into the medium-risk segment of the behavioral energy efficiency early warning range, the corresponding energy efficiency optimization early warning response scheme for that range is used as the final energy efficiency optimization early warning method. For example, for abnormal equipment energy efficiency operation status, the corresponding scheme is to automatically reduce the equipment operating power to the benchmark range and push the equipment parameter adjustment prompt, thereby achieving dual protection of energy saving and safety.

[0042] The real-time fire safety operation status of the target area is obtained by analyzing real-time fire energy efficiency monitoring data and dynamic safety perception data, specifically including the following steps: Based on the benchmark energy efficiency type, collaborative feature extraction is performed on real-time fire energy efficiency monitoring data and dynamic safety perception data to obtain the real-time fire safety operation status and operation status type. The real-time fire safety operation status includes equipment energy efficiency operation status, system collaborative operation status, emergency response preparedness status, and normal safe operation status.

[0043] Using a pre-defined baseline energy efficiency type as a classification framework, collaborative feature extraction is performed on real-time fire protection energy efficiency monitoring data and dynamic safety perception data. The baseline energy efficiency type includes equipment low-consumption safety type, system collaborative energy efficiency type, emergency response energy efficiency type, and normal operation baseline type. Each type corresponds to a specific data extraction dimension, ensuring that the extracted features cover both energy efficiency performance and safety status.

[0044] Taking the low-consumption safety type of equipment as an example, energy efficiency-related characteristics such as real-time power and energy consumption per unit time of fire-fighting equipment are extracted from real-time fire-fighting energy efficiency monitoring data. Safety-related characteristics such as operating temperature and pressure stability of the equipment are extracted from dynamic safety perception data. These characteristics are combined to obtain the real-time fire safety operating status of the corresponding equipment, and its operating status type is the equipment energy efficiency operating status. For example, a fire pump has a real-time power of 2.5 kW, an energy consumption per unit time of 1.2 kWh, an operating temperature of 42 degrees Celsius, and a pressure stability of 0.8 MPa. These characteristics together constitute the equipment energy efficiency operating status of the pump.

[0045] For the system collaborative energy efficiency type, energy efficiency characteristics such as the response time of multi-system linkage and the comprehensive energy consumption during the linkage process are extracted from real-time fire energy efficiency monitoring data. The changes in safety parameters of the target area after linkage are extracted from dynamic safety perception data. For example, when the alarm system and the sprinkler system are linked, the linkage response time is extracted to be 4 seconds and the linkage energy consumption is 3 kWh. At the same time, the safety characteristics of the smoke concentration in the area after linkage are extracted, which is reduced from 500 ppm to 200 ppm. The real-time fire safety operation status obtained by combining these data is the system collaborative operation status.

[0046] Based on the energy efficiency type of emergency response, the energy efficiency characteristics of emergency equipment, such as start-up preparation time and standby energy consumption, are extracted from real-time fire energy efficiency monitoring data. The safety characteristics of standby status integrity rate of emergency equipment are extracted from dynamic safety perception data. For example, the start-up preparation time of fire emergency fan is 3 seconds, the standby energy consumption is 0.5 kWh, and its standby status integrity rate is 98%. The real-time fire safety operation status type corresponding to these characteristics is emergency response preparation status.

[0047] Energy efficiency characteristics such as the average daily energy consumption during daily standby and the energy consumption during inspection are extracted from real-time fire energy efficiency monitoring data. Safety characteristics such as the daily operating status integrity rate of each device in the system are extracted from dynamic safety perception data. For example, the average daily energy consumption of the fire system during daily standby is 1.8 kWh, the energy consumption during inspection is 0.6 kWh, and the daily status integrity rate of the equipment is 95%. The real-time fire safety operation status type constituted by these characteristics is the normal safe operation status.

[0048] The target energy efficiency level is obtained by analyzing the real-time fire safety operation status and the fire safety energy efficiency level; the target fire safety energy efficiency standard is obtained by analyzing the real-time fire safety operation status and the energy efficiency standard nodes. This process includes the following steps: The operation status type corresponding to the real-time fire safety operation status is matched with the benchmark energy efficiency type corresponding to the fire safety energy efficiency level. The fire safety energy efficiency level that matches the baseline energy efficiency type and the operating status type is marked as the target energy efficiency level corresponding to the real-time fire safety operating status. The real-time fire safety operation status is compared with the benchmark fire safety energy efficiency standards corresponding to the level nodes in the target energy efficiency level. The nodes in the target energy efficiency level that match the characteristics of real-time fire safety operation status are marked as energy efficiency benchmark nodes, and the benchmark fire safety energy efficiency standards corresponding to the energy efficiency benchmark nodes are marked as target fire safety energy efficiency standards.

[0049] The system performs dimensional matching between the real-time fire safety operation status and the corresponding operation status type, and the corresponding baseline energy efficiency type for the fire safety energy efficiency level. The baseline energy efficiency type for the fire safety energy efficiency level includes low-consumption safety type for equipment and collaborative energy efficiency type for the system, while the operation status type includes equipment energy efficiency operation status and system collaborative operation status. These are corresponding type systems. For example, if the real-time fire safety operation status is equipment energy efficiency operation status, the corresponding low-consumption safety type for equipment is found within the baseline energy efficiency type of the fire safety energy efficiency level, thus completing the dimensional matching.

[0050] The fire safety energy efficiency level that matches the baseline energy efficiency type and the operating status type is marked as the target energy efficiency level corresponding to the real-time fire safety operating status. If the baseline energy efficiency type matching the equipment energy efficiency operating status is the equipment low-consumption safety type, and the corresponding fire safety energy efficiency level is the equipment low-consumption safety level, then the equipment low-consumption safety level is marked as the target energy efficiency level for the current real-time operating status; if the real-time operating status type is the system collaborative operating status, and the corresponding baseline energy efficiency type is the system collaborative energy efficiency type, then the system collaborative energy efficiency level is the target energy efficiency level.

[0051] The real-time fire safety operation status is compared with the benchmark fire safety energy efficiency standards corresponding to the level nodes in the target energy efficiency level. The target energy efficiency level contains multiple level nodes, each corresponding to a specific benchmark fire safety energy efficiency standard. Taking the low-power safety level of equipment as an example, this level contains three level nodes, each corresponding to different thresholds of the low-power safety benchmark of equipment. For example, the first-level node corresponds to a benchmark power of 2 kW and a benchmark pressure of 0.8 MPa, the second-level node corresponds to a benchmark power of 2.5 kW and a benchmark pressure of 0.7 MPa, and the third-level node corresponds to a benchmark power of 3 kW and a benchmark pressure of 0.6 MPa.

[0052] The nodes in the target energy efficiency hierarchy that match the real-time fire safety operation status characteristics are marked as energy efficiency benchmark nodes, and the benchmark fire safety energy efficiency standards corresponding to these nodes are marked as target fire safety energy efficiency standards. Feature fit = (1 - |real-time energy efficiency characteristics - benchmark energy efficiency characteristics| / benchmark energy efficiency characteristics) × 0.6 + (1 - |real-time safety characteristics - benchmark safety characteristics| / benchmark safety characteristics) × 0.4.

[0053] Energy efficiency-related early warning assessment intervals include behavioral energy efficiency early warning intervals, hierarchical energy efficiency early warning intervals, and overall energy efficiency early warning intervals. There are corresponding energy efficiency optimization warning response plans for each behavioral energy efficiency warning range. Each energy efficiency warning range corresponds to a corresponding energy-saving safety warning response plan. The energy efficiency warning range for the entire region corresponds to a corresponding energy conservation warning response plan for the entire region.

[0054] The energy efficiency-related early warning assessment range includes behavioral energy efficiency early warning ranges, hierarchical energy efficiency early warning ranges, and global energy efficiency early warning ranges. Behavioral energy efficiency early warning ranges target energy efficiency anomalies in a single operational behavior dimension, such as a fire-fighting equipment's real-time operational energy efficiency deviating from the benchmark. This range is pre-divided into different numerical ranges to accommodate different levels of anomalies; for example, a low-risk range is set at an energy efficiency loss anomaly coefficient below 0.2, a medium-risk range at 0.2 to 0.5, and a high-risk range above 0.5. Simultaneously, each range corresponds to a specific energy efficiency optimization early warning response plan: if the energy efficiency loss anomaly coefficient is in the low-risk range, the corresponding plan is to fine-tune the equipment's operating parameters to optimize energy consumption; if it is in the medium-risk range, the plan is to activate the equipment's energy-saving mode; if it is in the high-risk range, the plan is to push an equipment maintenance reminder and temporarily switch to backup equipment.

[0055] The tiered energy efficiency warning range targets anomalies at the fire safety energy efficiency level, such as deviations in overall energy efficiency at the low-consumption safety level of equipment or the system collaborative energy efficiency level. This range is defined by the characteristics of the tiered energy efficiency anomaly coefficient; for example, a low-risk range is below 0.3, a medium-risk range is 0.3 to 0.6, and a high-risk range is above 0.6. The corresponding tiered energy-saving safety warning response plan is tied to the specific tier: taking the system collaborative energy efficiency level as an example, if the tiered energy efficiency anomaly coefficient is in the medium-risk range, the plan might be to adjust the linkage sequence of multiple systems to reduce collaborative energy consumption; if it is in the high-risk range, the plan might be to suspend the linkage of non-core systems and send a system debugging notification.

[0056] The overall energy efficiency warning range targets the overall energy efficiency anomalies of the fire protection system in the target area, covering the comprehensive performance of all energy efficiency levels. This range is divided based on the overall fire safety anomaly coefficient; for example, the low-risk range is below 0.4, the medium-risk range is 0.4 to 0.7, and the high-risk range is above 0.7. The corresponding overall energy-saving warning response plan is a comprehensive measure for the entire area. If the overall fire safety anomaly coefficient is in the medium-risk range, the plan optimizes the overall operational sequence of the fire protection system within the area; if it is in the high-risk range, the plan activates the emergency energy-saving plan for the area's fire protection system and coordinates with maintenance personnel to conduct a comprehensive inspection.

[0057] The process of determining abnormal fire safety operating conditions and energy efficiency loss anomalies by analyzing real-time fire safety operating status and target fire safety energy efficiency standards includes the following steps: Compare the real-time fire safety operation status with the target fire safety energy efficiency standards to determine their feature fit. If the real-time fire safety operation status is less than the set threshold in terms of the characteristic conformity with the target fire safety energy efficiency standard, then the real-time fire safety operation status will be marked as an abnormal fire safety operation status. Obtain the energy efficiency deviation value and safety deviation value between the abnormal fire safety operation status and the target fire safety energy efficiency standard, and obtain the comprehensive abnormal deviation value based on the energy efficiency deviation value and safety deviation value; Based on the operation status type of the abnormal fire safety operation status, set the first energy efficiency weight of the abnormal fire safety operation status; obtain the energy efficiency redundancy ratio corresponding to the abnormal fire safety operation status, and set the second energy efficiency weight of the abnormal fire safety operation status based on the energy efficiency redundancy ratio; The energy efficiency loss weights for abnormal fire safety operation states are obtained based on the first and second energy efficiency weights. The energy efficiency loss anomaly coefficient is obtained based on the energy efficiency loss weight and the comprehensive anomaly deviation value.

[0058] The real-time fire safety operation status is compared with the target fire safety energy efficiency standard in terms of feature fit. Feature fit is a quantification of the degree of matching between the two in terms of energy efficiency and safety dimensions. For example, it compares the matching of real-time power with the target reference power and real-time pressure stability with the target reference stability for equipment energy efficiency operation status. A feature fit threshold is preset. If the feature fit is lower than the preset threshold, the current real-time fire safety operation status is marked as an abnormal fire safety operation status. For example, the reference power of a fire pump in the target fire safety energy efficiency standard is 2.5 kW and the reference pressure is 0.7 MPa. The real-time operating power is 3 kW and the pressure is 0.6 MPa. If the feature fit is 0.7 and the set threshold is 0.8, the fit is lower than the threshold, and the status is marked as an abnormal fire safety operation status.

[0059] Obtain the energy efficiency deviation and safety deviation values ​​between the abnormal fire safety operation status and the target fire safety energy efficiency standard. The energy efficiency deviation value is the difference between the real-time energy efficiency characteristic and the target benchmark energy efficiency characteristic. For example, the energy efficiency deviation value of a water pump is 3-2.5=0.5 kW. The safety deviation value is the difference between the real-time safety characteristic and the target benchmark safety characteristic, i.e., 0.6-0.7=-0.1 MPa. A negative value indicates that the safety characteristic is lower than the benchmark. The comprehensive abnormal deviation value = energy efficiency deviation value × 0.6 + safety deviation value × 0.4. 0.6 and 0.4 are the weights of energy efficiency and safety deviations, reflecting their importance in the abnormal assessment. The calculated comprehensive abnormal deviation value is 0.5×0.6+(-0.1)×0.4=0.3-0.04=0.26.

[0060] Set a first energy efficiency weight and a second energy efficiency weight. The first energy efficiency weight is set based on the operating state type of abnormal fire safety operation. For example, the first energy efficiency weight corresponding to the equipment's energy efficiency operation state is 0.7. The second energy efficiency weight is set based on the energy efficiency redundancy ratio corresponding to the abnormal state. If the energy efficiency redundancy ratio of the water pump is 20%, and the energy efficiency redundancy ratio is the proportion of the equipment's allowable energy efficiency fluctuation range to the benchmark energy efficiency, then the second energy efficiency weight is 0.3. Energy efficiency loss weight = first energy efficiency weight × 0.8 + second energy efficiency weight × 0.2, where 0.8 and 0.2 are the weights of the two types of weights. Substituting the data, the energy efficiency loss weight is 0.7 × 0.8 + 0.3 × 0.2 = 0.56 + 0.06 = 0.62.

[0061] The energy efficiency loss anomaly coefficient = energy efficiency loss weight × comprehensive anomaly deviation value. Substituting the data, the energy efficiency loss anomaly coefficient is calculated to be 0.62 × 0.26 ≈ 0.1612.

[0062] The energy efficiency loss anomaly coefficient is matched with the behavioral energy efficiency early warning interval. Assuming the low-risk segment of the behavioral energy efficiency early warning interval is 0 to 0.2 and the medium-risk segment is 0.2 to 0.5, then 0.1612 belongs to the low-risk segment. In this case, the energy efficiency optimization early warning response scheme corresponding to this interval is marked as the energy efficiency optimization early warning method. For example, for the abnormal state of the water pump, the corresponding scheme is to fine-tune the water pump's operating power to the reference range, thereby achieving a balance between energy saving and safety.

[0063] The energy efficiency optimization early warning method is derived based on the energy efficiency correlation early warning assessment interval and the energy efficiency loss anomaly coefficient, specifically including the following steps: The coefficients of abnormal energy efficiency loss corresponding to abnormal fire safety operation status are matched with the behavioral energy efficiency early warning interval. The energy efficiency optimization early warning response scheme corresponding to the energy efficiency early warning interval to which the energy efficiency loss anomaly coefficient belongs is marked as the energy efficiency optimization early warning method.

[0064] The energy efficiency loss anomaly coefficient corresponding to abnormal fire safety operation status is matched with the behavioral energy efficiency warning interval. The behavioral energy efficiency warning interval is a pre-defined numerical range based on the severity of the energy efficiency anomaly; different ranges correspond to different risk levels. For example, a low-risk interval is set at 0 to 0.2, a medium-risk interval at 0.2 to 0.5, and a high-risk interval above 0.5. The interval division is based on the analysis of a large amount of historical energy efficiency anomaly data and its corresponding safety impacts, ensuring that the risk level of each interval matches the actual response requirements.

[0065] Once the abnormal energy efficiency loss coefficient is determined to belong to the behavioral energy efficiency warning range, the corresponding energy efficiency optimization warning response scheme for that range is invoked and marked as the final energy efficiency optimization warning method. If the abnormal energy efficiency loss coefficient corresponding to an abnormal fire safety operation state is 0.15, and after matching it belongs to the low-risk range, the corresponding energy efficiency optimization warning response scheme is to automatically fine-tune the operating parameters of the equipment, such as reducing the real-time power of the fire pump from 3 kW to 2.6 kW to approach the benchmark power in the target fire safety energy efficiency standard, while maintaining the safe operation state of the equipment; if the abnormal energy efficiency loss coefficient is 0.3, belonging to the medium-risk range, the corresponding scheme is to activate the energy-saving mode of the equipment, such as reducing the operating frequency of the equipment to reduce energy consumption under the premise of ensuring safety; if the abnormal energy efficiency loss coefficient is 0.6, belonging to the high-risk range, the corresponding scheme may be to push the equipment maintenance reminder and temporarily switch to the operation of the backup equipment to prevent the abnormal state from continuing to expand and affecting system safety.

[0066] The overall fire safety anomaly coefficient for the target area is obtained based on the hierarchical energy efficiency anomaly coefficient and the safety priority weight; the overall energy-saving early warning method for the target area is obtained based on the energy efficiency correlation early warning assessment interval and the overall fire safety anomaly coefficient, specifically including the following steps: Set the safety priority weight of the fire safety energy efficiency level based on the benchmark energy efficiency type corresponding to the fire safety energy efficiency level; The overall contribution anomaly coefficient is obtained by weighting the hierarchical energy efficiency anomaly coefficients according to the safety priority weights. The total fire safety anomaly coefficient of the target area is obtained by summing the total contribution anomaly coefficients of the fire safety energy efficiency levels. Compare the overall fire safety anomaly coefficient of the target area with the overall energy efficiency early warning interval; If the overall fire safety anomaly coefficient corresponding to the target area is not within the overall energy efficiency warning range, then there is no need to issue an overall fire safety warning for the target area; if the overall fire safety anomaly coefficient corresponding to the target area is within the overall energy efficiency warning range, then there is a need to issue an overall fire safety warning for the target area. The energy-saving early warning response scheme corresponding to the energy efficiency early warning interval of the entire region to which the abnormal coefficient of the entire region fire safety belongs is marked as the energy-saving early warning method of the entire region.

[0067] The safety priority weights for each level are set based on the baseline energy efficiency type corresponding to the fire safety energy efficiency levels. Different baseline energy efficiency types correspond to different levels of importance within the fire safety system, hence the weights vary. For example, the emergency response energy efficiency level directly relates to the ability to handle emergencies, with a safety priority weight of 0.4; the system coordination energy efficiency level affects the reliability of multi-system linkage, with a weight of 0.3; the equipment low-consumption safety level relates to the stable operation of individual devices, with a weight of 0.2; and the normal operation energy efficiency level corresponds to daily standby status, with a weight of 0.1. The sum of these weights is 1, ensuring a reasonable contribution ratio for each level.

[0068] The hierarchical energy efficiency anomaly coefficient corresponding to each fire safety energy efficiency level is weighted according to the safety priority weight to obtain the overall contribution anomaly coefficient. If the hierarchical energy efficiency anomaly coefficient of the emergency response energy efficiency level is 0.6, the system coordination energy efficiency level is 0.5, the equipment low-consumption safety level is 0.3, and the normal operation energy efficiency level is 0.2, the overall contribution anomaly coefficient = hierarchical energy efficiency anomaly coefficient × safety priority weight. The overall contribution anomaly coefficient of each level is calculated as follows: emergency response level is 0.6 × 0.4 = 0.24, system coordination level is 0.5 × 0.3 = 0.15, equipment low-consumption level is 0.3 × 0.2 = 0.06, and normal operation level is 0.2 × 0.1 = 0.02.

[0069] The overall fire safety anomaly coefficient for the target area is obtained by summing the overall contribution anomaly coefficients at each level. The overall fire safety anomaly coefficient is 0.24 + 0.15 + 0.06 + 0.02 = 0.47. The overall fire safety anomaly coefficient comprehensively reflects the degree of energy efficiency anomaly in the target area.

[0070] The overall fire safety anomaly coefficient is compared with the overall energy efficiency warning range. The overall energy efficiency warning range is a preset risk range, such as 0.3 to 0.5 for low risk, 0.5 to 0.7 for medium risk, and above 0.7 for high risk. If the overall fire safety anomaly coefficient is 0.47, falling into the low risk range, then an overall fire safety warning for the target area is required; if the coefficient is below 0.3, then an overall warning is not required.

[0071] The corresponding energy-saving early warning response plan for the range of the overall fire safety anomaly coefficient is marked as the overall energy-saving early warning method. For example, 0.47 belongs to the low-risk range, and the corresponding plan is to optimize the daily operation sequence of the fire protection system in the area and adjust the standby interval of each device to reduce overall energy consumption; if the coefficient falls into the medium-risk range, the plan is to suspend the operation of non-core fire protection subsystems in the area and push a system collaborative debugging prompt; if it falls into the high-risk range, the plan is to activate the emergency standby mode of the regional fire protection system and coordinate the operation and maintenance team to carry out a full-area inspection.

[0072] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy-saving fire safety early warning system based on data sharing on an internet platform, characterized in that: include: Acquisition module: Acquires historical energy efficiency data and safety event data of the fire protection system in the target area, and acquires real-time fire energy efficiency monitoring data and dynamic safety perception data of the target area; Generation module: Obtains benchmark fire safety energy efficiency standards through historical energy efficiency data and safety event data, generates energy efficiency standard nodes based on benchmark fire safety energy efficiency standards, and generates fire safety energy efficiency levels based on energy efficiency standard nodes; The first processing module processes real-time fire energy efficiency monitoring data, dynamic safety perception data, fire safety energy efficiency levels, and energy efficiency standard nodes to obtain an energy efficiency optimization early warning method. The second processing module: obtains the hierarchical energy efficiency anomaly coefficient corresponding to the fire safety energy efficiency level based on the energy efficiency loss anomaly coefficient; obtains the risk energy efficiency early warning level and the hierarchical energy-saving safety early warning method based on the energy efficiency correlation early warning assessment interval and the hierarchical energy efficiency anomaly coefficient. The third processing module: obtains the overall fire safety anomaly coefficient of the target area based on the hierarchical energy efficiency anomaly coefficient and the safety priority weight; and obtains the overall energy-saving early warning method of the target area based on the energy efficiency correlation early warning assessment interval and the overall fire safety anomaly coefficient. Output module: Outputs energy-saving fire safety early warning results for the target area based on energy efficiency optimization early warning method, hierarchical energy-saving safety early warning method, and whole-area energy-saving early warning method.

2. The energy-saving fire safety early warning system based on internet platform data sharing as described in claim 1, characterized in that, The baseline fire safety energy efficiency standard is obtained by combining historical energy efficiency data and safety incident data, specifically including the following steps: Set a benchmark energy efficiency type, wherein the benchmark energy efficiency type includes equipment low-consumption safety type, system collaborative energy efficiency type, emergency response energy efficiency type and normal operation benchmark type; Based on the benchmark energy efficiency type, historical energy efficiency data and safety incident data of the fire protection system are extracted to obtain the benchmark fire safety energy efficiency standard; The benchmark fire safety energy efficiency standards include low-consumption safety benchmarks for equipment, system collaborative energy efficiency benchmarks, emergency response energy efficiency benchmarks, and routine operation energy efficiency benchmarks.

3. The energy-saving fire safety early warning system based on internet platform data sharing as described in claim 1, characterized in that, The fire safety energy efficiency levels include low-consumption safety level of equipment, system coordination energy efficiency level, emergency response energy efficiency level, and normal operation energy efficiency level.

4. The energy-saving fire safety early warning system based on internet platform data sharing as described in claim 1, characterized in that, The energy efficiency optimization and early warning method is obtained by processing real-time fire energy efficiency monitoring data, dynamic safety perception data, fire safety energy efficiency levels, and energy efficiency standard nodes. The specific steps include: The real-time fire safety operation status of the target area is obtained by analyzing real-time fire energy efficiency monitoring data and dynamic safety perception data. The target energy efficiency level is obtained by analyzing the real-time fire safety operation status and the fire safety energy efficiency level; the target fire safety energy efficiency standard is obtained by analyzing the real-time fire safety operation status and the energy efficiency standard node. The abnormal fire safety operation status and the abnormal coefficient of energy efficiency loss are obtained by judging the real-time fire safety operation status and the target fire safety energy efficiency standard. An energy efficiency optimization early warning method is derived based on the energy efficiency correlation early warning assessment interval and the energy efficiency loss anomaly coefficient.

5. The energy-saving fire safety early warning system based on Internet platform data sharing as described in claim 4, characterized in that, The real-time fire safety operation status of the target area is obtained by analyzing real-time fire energy efficiency monitoring data and dynamic safety perception data, specifically including the following steps: Based on the benchmark energy efficiency type, collaborative feature extraction is performed on real-time fire energy efficiency monitoring data and dynamic safety perception data to obtain the real-time fire safety operation status and operation status type. The real-time fire safety operation status includes equipment energy efficiency operation status, system collaborative operation status, emergency response preparedness status, and normal safe operation status.

6. The energy-saving fire safety early warning system based on Internet platform data sharing as described in claim 5, characterized in that, The target energy efficiency level is obtained by analyzing the real-time fire safety operation status and fire safety energy efficiency level. The target fire safety energy efficiency standard is obtained by analyzing the real-time fire safety operation status and energy efficiency standard nodes. The specific steps include: The operation status type corresponding to the real-time fire safety operation status is matched with the benchmark energy efficiency type corresponding to the fire safety energy efficiency level. The fire safety energy efficiency level that matches the baseline energy efficiency type and the operating status type is marked as the target energy efficiency level corresponding to the real-time fire safety operating status. The real-time fire safety operation status is compared with the benchmark fire safety energy efficiency standards corresponding to the level nodes in the target energy efficiency level. The nodes in the target energy efficiency level that match the characteristics of real-time fire safety operation status are marked as energy efficiency benchmark nodes, and the benchmark fire safety energy efficiency standards corresponding to the energy efficiency benchmark nodes are marked as target fire safety energy efficiency standards.

7. The energy-saving fire safety early warning system based on Internet platform data sharing as described in claim 6, characterized in that, The energy efficiency-related early warning assessment intervals include behavioral energy efficiency early warning intervals, hierarchical energy efficiency early warning intervals, and global energy efficiency early warning intervals. There are corresponding energy efficiency optimization warning response plans for each behavioral energy efficiency warning range. Each energy efficiency warning range corresponds to a corresponding energy-saving safety warning response plan. The energy efficiency warning range for the entire region corresponds to a corresponding energy conservation warning response plan for the entire region.

8. The energy-saving fire safety early warning system based on Internet platform data sharing according to claim 7, characterized in that, The process of determining abnormal fire safety operating conditions and energy efficiency loss anomalies by analyzing real-time fire safety operating status and target fire safety energy efficiency standards includes the following steps: Compare the real-time fire safety operation status with the target fire safety energy efficiency standards to determine their feature fit. If the real-time fire safety operation status is less than the set threshold in terms of the characteristic conformity with the target fire safety energy efficiency standard, then the real-time fire safety operation status will be marked as an abnormal fire safety operation status. Obtain the energy efficiency deviation value and safety deviation value between the abnormal fire safety operation status and the target fire safety energy efficiency standard, and obtain the comprehensive abnormal deviation value based on the energy efficiency deviation value and safety deviation value; Based on the operation status type of the abnormal fire safety operation status, set the first energy efficiency weight of the abnormal fire safety operation status; obtain the energy efficiency redundancy ratio corresponding to the abnormal fire safety operation status, and set the second energy efficiency weight of the abnormal fire safety operation status based on the energy efficiency redundancy ratio; The energy efficiency loss weights for abnormal fire safety operation states are obtained based on the first and second energy efficiency weights. The energy efficiency loss anomaly coefficient is obtained based on the energy efficiency loss weight and the comprehensive anomaly deviation value.

9. The energy-saving fire safety early warning system based on Internet platform data sharing according to claim 4, characterized in that, The energy efficiency optimization early warning method is derived based on the energy efficiency correlation early warning assessment interval and the energy efficiency loss anomaly coefficient, specifically including the following steps: The coefficients of abnormal energy efficiency loss corresponding to abnormal fire safety operation status are matched with the behavioral energy efficiency early warning interval. The energy efficiency optimization early warning response scheme corresponding to the energy efficiency early warning interval to which the energy efficiency loss anomaly coefficient belongs is marked as the energy efficiency optimization early warning method.

10. The energy-saving fire safety early warning system based on Internet platform data sharing according to claim 1, characterized in that, The overall fire safety anomaly coefficient for the target area is obtained based on the hierarchical energy efficiency anomaly coefficient and the safety priority weight; the overall energy-saving early warning method for the target area is obtained based on the energy efficiency correlation early warning assessment interval and the overall fire safety anomaly coefficient, specifically including the following steps: Set the safety priority weight of the fire safety energy efficiency level based on the benchmark energy efficiency type corresponding to the fire safety energy efficiency level; The overall contribution anomaly coefficient is obtained by weighting the hierarchical energy efficiency anomaly coefficients according to the safety priority weights. The total contribution anomaly coefficients of the fire safety energy efficiency levels are accumulated to obtain the total fire safety anomaly coefficient of the target area. Compare the overall fire safety anomaly coefficient of the target area with the overall energy efficiency early warning interval; If the overall fire safety anomaly coefficient corresponding to the target area is not within the overall energy efficiency warning range, then there is no need to issue an overall fire safety warning for the target area; if the overall fire safety anomaly coefficient corresponding to the target area is within the overall energy efficiency warning range, then there is a need to issue an overall fire safety warning for the target area. The energy-saving early warning response scheme corresponding to the energy efficiency early warning interval of the entire region to which the abnormal coefficient of the entire region fire safety belongs is marked as the energy-saving early warning method of the entire region.

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