An energy storage management system for construction safety monitoring equipment

Abstract

The present invention belongs to the technical field of power management, and specifically discloses an energy storage management system for construction safety monitoring equipment, comprising: a data interaction unit for acquiring working data of a sensor and environmental data that affects the working state of the sensor; a data analysis unit for acquiring working data and environmental data and generating analysis data through a built-in prediction model; an energy optimization unit for generating control data for adjusting the working state of the sensor according to the analysis data and a preset judgment rule; a communication control unit for sending control data to the sensor and sending working data, environmental data and control data to a control end; and the control end for transmitting an instruction for adjusting the judgment rule through the communication control unit. The present invention has the following advantages: by real-time monitoring and dynamic adjustment of the working mode of the sensor, energy consumption can be optimized, downtime can be prevented, and the life of the equipment can be extended.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to an energy storage management system for building construction safety monitoring equipment. Background Art

[0002] Construction refers to production activities during the implementation phase of engineering construction, including foundation construction, main structure construction, roof construction, decoration construction, etc. During the construction process, people always focus on project quality, safety (including environmental protection), progress and cost. However, the existing online risk detection system for construction quality and safety is not accurate in wall detection; at the same time, the tilt angle is manually analyzed and judged by specialized monitoring personnel, which not only sharply increases the labor cost of the building monitoring system, but also causes calculation deviations due to the subjectivity and inaccuracy of manual judgment.

[0003] In order to solve the subjective problem of manual judgment, a search found that there is a construction safety monitoring method and system with an existing publication number of CN117196892A, which includes a full-stage division module, a safety monitoring feature determination module, a response date marking module, a trigger analysis module and an early warning reminder module; the full-stage division module is used to obtain the full-stage process of the construction, with each construction stage as a unit construction stage and the construction period corresponding to each construction stage as a unit monitoring period; the safety monitoring feature determination module is used to determine the safety monitoring features of the unit construction stage; the response date marking module is used to mark the intersection date of the intersecting unit monitoring periods and the connection date of the continuous adjacent unit monitoring periods as the response date; the trigger analysis module is used to trigger the correlation analysis signal for correlation analysis when the response date is reached, and output the key monitoring features of each newly added unit construction stage; the early warning reminder module is used to transmit early warning signals for key monitoring features.

[0004] Although the aforementioned technical solution realizes detection through the coordination of multiple modules, sensors are still needed between the multiple modules to collect data. Since the sensors require electrical energy to maintain, they need to be charged. In order to avoid excessive costs, sensors are generally used with built-in batteries, which leads to limited use time of data collection sensors. Currently, there are no modules for energy storage detection and control of sensors in the system for monitoring construction. This results in the sensors still working even when there is no construction on the site, resulting in a waste of energy. The sensors are not discovered until they are exhausted, and data loss is caused by sensor shutdown.

[0005] Therefore, an energy storage management system for construction safety monitoring equipment is proposed to solve the above-mentioned problems. Summary of the invention

[0006] The present invention aims to provide an energy storage management system for construction safety monitoring equipment to solve or improve at least one of the above-mentioned technical problems.

[0007] In view of this, a first aspect of the present invention is to provide an energy storage management system for construction safety monitoring equipment.

[0008] The first aspect of the present invention provides an energy storage management system for construction safety monitoring equipment, comprising: a data interaction unit, connected to a sensor deployed in a construction site, for obtaining the working data of the sensor and environmental data that affects the working state of the sensor; a data analysis unit, for obtaining the working data and the environmental data and generating analysis data through a built-in prediction model; an energy optimization unit, for generating control data for adjusting the working state of the sensor based on the analysis data and preset judgment rules; a communication control unit, connected to a control end and the sensor, respectively, and sending the control data to the sensor and sending the working data, the environmental data and the control data to the control end; the control end transmits instructions for adjusting the judgment rules through the communication control unit.

[0009] In any of the above technical solutions, the data exchange unit is connected to at least one of the n sensors via a wireless network, and the sensors exchange data via a wired network and / or a wireless network.

[0010] In any of the above technical solutions, the working data includes the monitoring data of the construction site collected by the sensor and the energy consumption data of the energy storage module inside the sensor, and the data interaction unit includes: a data acquisition module, used to obtain the environmental data and the monitoring data; a data transceiver module, used to receive the energy consumption data, and send control data to the sensor through a wired network and / or a wireless network.

[0011] In any of the above technical solutions, the data analysis unit includes a preprocessing module, which packages the environmental data and the energy consumption data into preprocessed data, specifically the following formula: D=(E, P, T, H); wherein D is the preprocessing data, used to characterize the rated power consumption of the energy storage module; E is the remaining power of the energy storage module; P is the power consumption of the energy storage module in the current time period; T is the temperature value contained in the environmental data; and H is the humidity value contained in the environmental data.

[0012] In any of the above technical solutions, the power consumption is a value of the rated power consumption of the sensor in the current working state after being affected by the temperature value and the humidity value in the environmental data.

[0013] In any of the above technical solutions, the prediction model is specifically the following formula: φ(B)(1-B) d Y t+1 =θ(B)e t+1 ; Wherein, t is the time period; Y t+1 is the observed value in the next time period, and the observed value is assigned by the rated power consumption, and the Y t+1 The estimated value corresponding to the environmental data is obtained by the Y t The corresponding power consumption and rated power consumption are generated; B is the back-shift operator, and BY t+1 =Y t ; d is the differential order; e t+1 is a white noise sequence used to characterize random disturbances; φ(B) is an autoregressive polynomial, and φ(B)=1-φ1B-φ2B 2 -…-φ p B p ; p is the order of the autoregressive term; θ(B) is a moving average polynomial, and θ(B) = 1 + θ1B + θ2B 2 +…+θ q B q ; The q is the order of the moving average term.

[0014] In any of the above technical solutions, the data analysis unit also includes: a power prediction module, which is used to obtain pre-processed data of multiple connected time periods including the current time period, and generate the rated power consumption of the energy storage module in the next time period through the prediction model; an energy analysis module, which is used to determine whether the rated power consumption of the next time period and the estimated value of the environmental data match the working status of the sensor in the current time period, and generate the analysis data.

[0015] In any of the above technical solutions, the working states of all the sensors are the same in the same time period, and the energy analysis module generates the analysis data by the matching number of the rated power consumption in the next time period and the working state in the current time period.

[0016] In any of the above technical solutions, the energy optimization unit includes: a user setting module, connected to the control end; the user setting module adjusts the judgment rule according to the instruction to adapt to the temperature and humidity of the current environment of the sensor; a judgment control module, used to judge whether the analysis data of the current time period is qualified through the adjusted judgment rule, and generate the control data through the judgment result.

[0017] In any of the above technical solutions, the judgment control module generates the control data through the following formula: M = arg min M∈1.2...Nf(D); wherein, M is the working state, and N is the number of working states; argmin is the working state M that makes the objective function f(D) reach the minimum value; f(D) is the number of matches between the rated power consumption in the next time period and the working state in the current time period.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] By continuously monitoring the energy consumption status of the sensor, the system can understand the energy usage status of each sensor in real time. According to the real-time energy consumption data and environmental impact of the sensor, the energy optimization unit can dynamically adjust the working mode of the sensor to reduce energy consumption during non-essential times. The data analysis unit predicts the possible maintenance needs of the sensor and performs preventive maintenance to avoid sudden shutdown due to battery exhaustion.

[0020] By adjusting the working status of the sensor, unnecessary energy consumption can be significantly reduced and energy efficiency can be improved. Through predictive maintenance and avoiding sudden shutdowns due to battery exhaustion, maintenance costs and downtime can be reduced. It ensures that the sensor is in the best working condition during critical working hours, avoids data loss and monitoring interruptions, and extends the service life of the sensor and the entire monitoring system through effective energy management and maintenance strategies.

[0021] Additional aspects and advantages of embodiments according to the present invention will become apparent in the following description or may be learned through practice of embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0023] Figure 1 It is a structural logic block diagram of the present invention. DETAILED DESCRIPTION

[0024] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0026] See also Figure 1 , the following describes an energy storage management system for construction safety monitoring equipment according to some embodiments of the present invention.

[0027] The embodiment of the first aspect of the present invention provides an energy storage management system for construction safety monitoring equipment. In some embodiments of the present invention, such as Figure 1 As shown, the energy storage management system includes:

[0028] The data interaction unit is connected to the sensors deployed in the construction site and is used to obtain the working data of the sensors and the environmental data that affects the working status of the sensors.

[0029] The data analysis unit acquires work data and environmental data and generates analytical data through built-in predictive models.

[0030] The energy optimization unit generates control data for adjusting the working state of the sensor based on the analysis data and preset judgment rules.

[0031] The communication control unit is connected to the control end and the sensor respectively, and sends control data to the sensor and sends working data, environmental data and control data to the control end; the control end transmits instructions for adjusting the judgment rules through the communication control unit.

[0032] The present invention provides an energy storage management system for construction safety monitoring equipment, in which the data interaction unit is responsible for obtaining working data and environmental data from various sensors deployed at the construction site. Working data may include the power status, operating efficiency and fault records of the sensor; environmental data may cover temperature, humidity, vibration intensity, etc., all of which directly affect the working efficiency and energy consumption of the sensor; it is not only responsible for collecting data from the sensor, but also sending this data to the central monitoring system for processing and analysis, as well as receiving responses and instructions from the central monitoring system to adjust the working status or configuration of the sensor.

[0033] The data exchange unit is connected to the sensors and the central monitoring system through wireless communication protocols (such as Wi-Fi, LoRa, Zigbee, etc.) or wired connections (such as Ethernet). This communication mechanism ensures timely data transmission and high reliability; it may contain data caching and preliminary processing functions to optimize data transmission and reduce the load on the central processing system. For example, it can perform preliminary screening and compression on the collected data, send full data only when necessary, or give priority to processing and reporting when critical events are detected. The data exchange unit connects to the sensors deployed in the construction site and is responsible for obtaining the working data of the sensors and the environmental data that affects the working status of the sensors. It collects key information about the battery power and operating efficiency of the sensors, and provides basic data for the energy management of the system.

[0034] The data analysis unit receives the working data and environmental data of the sensor from the data exchange unit. The working data may include the real-time status, energy consumption and performance indicators of the sensor; the environmental data includes external factors that affect the performance of the sensor, such as temperature, humidity, vibration, etc. Using the built-in prediction model, the unit analyzes the received data and generates more in-depth analysis data. These analysis data are intended to predict future equipment behavior, such as energy consumption trends, possible equipment failures, maintenance needs, etc.

[0035] The prediction model built into the data analysis unit is based on machine learning or statistical analysis techniques. The model is trained based on historical data and can evaluate and predict incoming real-time data; it implements real-time data processing to ensure rapid response and decision-making. It dynamically adjusts the operating parameters of the sensor based on the analysis results to optimize energy consumption and performance; it can also include a self-learning mechanism that enables the model to continuously optimize and adjust its own prediction accuracy based on new data. The data analysis unit receives work and environmental data from the data interaction unit and generates analysis data using the built-in prediction model. These analysis data include battery charge trends and energy consumption patterns, providing decision support for the energy optimization unit.

[0036] The energy optimization unit formulates control strategies based on the analysis data provided by the data analysis unit and the preset judgment rules. These strategies are designed to adjust the working state of the sensor to optimize energy use and improve monitoring efficiency; generate control data, which are specific instructions or parameters used to directly adjust the operation mode of the sensor, such as changing the sampling frequency, switching to low power mode, or starting real-time monitoring of important events.

[0037] The energy optimization unit contains a rule engine, which is a set of logical rules that define the actions to be taken under specific data input. For example, if the energy consumption of a sensor exceeds expectations, the rule may instruct the sensor to reduce the frequency of data transmission; the control data is not static, but dynamically generated based on real-time analysis results. This ensures that the system's response is based on the latest monitoring data and environmental conditions, thereby achieving true energy optimization; the operation of the energy optimization unit is not one-way, but a closed-loop control system. It adjusts the strategy based on the feedback of the execution results and optimizes the energy usage strategy for the next stage. The energy optimization unit generates control data for adjusting the working state of the sensor based on the analysis data provided by the data analysis unit and the preset judgment rules. These control data are designed to adjust the operation of the sensor and reduce invalid energy consumption. For example, when the power is lower than the preset threshold, the sensor is automatically put into sleep mode to extend battery life and prevent data loss.

[0038] The communication control unit is responsible for sending control data to the sensor in order to adjust its working state. At the same time, it is also responsible for feeding back working data, environmental data and control data to the control end; the unit receives instructions from the control end, which may include commands to adjust judgment rules or modify sensor configuration.

[0039] The communication control unit establishes a stable communication link with the control end and sensors through a two-way communication protocol (such as TCP / IP, MQTT, etc.). This allows data and instructions to flow between different parts of the system, ensuring timely information updates and system responsiveness; generates specific control signals based on the control data from the energy optimization unit, and transmits them to the sensors wirelessly or wired to guide them to adjust their working modes (such as switching from normal mode to energy-saving mode). At the same time, the unit is also responsible for receiving new instructions from the control end and adjusting the internal judgment rules or transmission strategies according to these instructions; integrating data from various sensors and performing necessary formatting, and then sending these data and the generated control data back to the control end for further analysis and monitoring. The communication control unit is the communication hub between the system and the sensors and control end, responsible for sending control data to the sensors, and transmitting working data, environmental data and control data back to the control end. In addition, it receives instructions from the control end, adjusts the judgment rules or sensor configuration according to these instructions, and ensures the adaptability and flexibility of the system.

[0040] In summary, the power level of each sensor is continuously monitored, energy consumption patterns are identified in real time, and the sensor is automatically adjusted to low power mode or sleep state before the power level falls below the safety threshold, thereby preventing accidental shutdown and performance degradation; maintenance personnel can obtain the sensor's monitoring data and battery status in real time through the communication control unit, and make timely adjustments to ensure continuous monitoring and efficient energy use at the construction site; through built-in models and intelligent judgment rules, the energy optimization unit not only reduces dependence on manual intervention, but also improves the system's response speed and accuracy.

[0041] In any of the above embodiments, the data exchange unit is connected to at least one of the n sensors via a wireless network, and the sensors exchange data via a wired network and / or a wireless network.

[0042] In this embodiment, the data interaction unit establishes a connection with at least one of the n sensors via a wireless network. This allows the unit to remotely collect the working data and environmental data of the sensor without physical contact; the sensors are not only connected to the data interaction unit, but can also exchange data with each other via a wired or wireless network. This design allows the sensor network to continue to transmit data through other sensors when the connection between a sensor and the data interaction unit fails.

[0043] The data interaction unit usually uses wireless technologies such as Wi-Fi, Bluetooth, Zigbee or LoRa to establish a connection with the sensor. These technologies have their own advantages. For example, Wi-Fi has a wide coverage range, while LoRa is suitable for long-distance low-power communication. In practical applications, system designers may choose to use wired networks (such as Ethernet) in combination with wireless networks to enhance the stability of the network and the reliability of data transmission. Wired connections are generally more stable and secure, while wireless connections provide greater flexibility and scalability. Data interaction between sensors allows them to forward data through other sensors in the network. This "multi-hop" network can expand the coverage and enhance the robustness of the network. The network established between sensors can automatically configure the optimal path for data and ensure data continuity even when some nodes fail.

[0044] In any of the above embodiments, the working data includes monitoring data of the construction site collected by the sensor and energy consumption data of the energy storage module inside the sensor, and the data interaction unit includes:

[0045] Data acquisition module, used to obtain environmental data and monitoring data.

[0046] The data transceiver module is used to receive energy consumption data and send control data to the sensor through a wired network and / or a wireless network.

[0047] In this embodiment, the data acquisition module is responsible for collecting various monitoring data of the construction site in real time, such as temperature, humidity, vibration, etc., which reflect the environmental conditions and possible safety risks of the construction site. In addition, it also includes energy consumption data collected from the energy storage module inside the sensor, such as battery power, energy consumption rate, etc., which is crucial for evaluating the operating efficiency and continuous monitoring capabilities of the sensor; data collected from different sensors need to be integrated for unified processing and analysis.

[0048] Through advanced data interfaces and protocols, the module is able to process data streams from a variety of sensors, including standardized data formats and synchronized timestamps of multiple data sources; before data is transmitted to the central system, necessary data filtering, denoising and preliminary analysis are performed to reduce the amount of data transmission and improve data processing efficiency.

[0049] The data transceiver module receives the energy consumption data of the energy storage module inside the sensor, which helps monitor the energy efficiency of the sensor and predict maintenance needs; based on the instructions of the central processing unit or energy optimization unit, it sends control data to the sensor through a wired or wireless network. These control data are used to adjust the working mode of the sensor, such as changing the data collection frequency, switching to energy-saving mode, or restarting the system.

[0050] Use efficient and reliable communication technologies, such as Ethernet, Wi-Fi or LoRa, to ensure timely transmission and reception of data. This two-way communication allows the system to not only receive sensor data, but also issue control commands to the sensors; it is responsible for data routing and network traffic management to ensure timely data transmission and control command delivery even when network conditions change or the network is congested.

[0051] Specifically, the specific steps of the data interaction unit are:

[0052] Determine data collection requirements: The target is battery power, energy consumption data, and external environmental conditions: temperature and humidity. Set the frequency of data collection to collect data every minute or every hour.

[0053] Configure sensors and data acquisition devices: Select sensors with battery power monitoring, energy consumption monitoring, and temperature and humidity monitoring functions;

[0054] Write a data acquisition program: According to the data interface and communication protocol of the sensor, write a data acquisition program to receive the data returned by the sensor, parse and process the received data, and extract the battery power, energy consumption data, temperature and humidity information;

[0055] Implement data collection: deploy the written data collection program to the data collection device, start the data collection program, and begin to collect the working status data of the sensor in real time.

[0056] In any of the above embodiments, the data analysis unit includes a preprocessing module, which packages the environmental data and the energy consumption data into preprocessed data, specifically the following formula:

[0057] D = (E, P, T, H);

[0058] Among them, D is preprocessed data, which is used to characterize the rated power consumption of the energy storage module; E is the remaining power of the energy storage module; P is the power consumption of the energy storage module in the current time period; T is the temperature value contained in the environmental data; H is the humidity value contained in the environmental data.

[0059] In this embodiment, the main function of the preprocessing module is to integrate and package the collected environmental data and energy consumption data into preprocessed data; various types of data, including the remaining power (E), power consumption (P), ambient temperature (T) and humidity (H) of the energy storage module, are combined into a data packet (D). This packaging method facilitates subsequent data analysis and decision-making processes; the preprocessed data (D) characterizes the performance and power consumption of the energy storage module under specific environmental conditions, providing a comprehensive perspective to analyze and optimize the energy consumption of the sensor.

[0060] The preprocessing module first formats the collected data to ensure data consistency and comparability. For example, temperature and humidity data may need to be collected from different sensors and converted into a unified unit of measurement. Through data fusion technology, information from multiple data sources is merged into a unified data structure. This involves data synchronization, ensuring that all data elements are from the same time point or time range; encapsulating the processed data into a structured form, which not only facilitates data storage and transmission, but also makes subsequent data analysis more efficient and accurate.

[0061] In any of the above embodiments, the power consumption is a value of the rated power consumption of the sensor in the current working state after being affected by the temperature value and the humidity value in the environmental data.

[0062] In this embodiment, the actual power consumption is calculated based on the current working state of the sensor and environmental factors (such as temperature and humidity). Environmental conditions can significantly affect the energy efficiency and power consumption of the sensor; the operating parameters of the sensor are adjusted according to the environmental data to optimize energy consumption and ensure that the sensor can operate efficiently in different environments.

[0063] Temperature can affect the power consumption of sensors through a variety of mechanisms. For example, higher temperatures may increase the internal resistance of electronic devices, resulting in increased power consumption; or in cold environments, batteries discharge less efficiently, also requiring more energy to maintain normal operation; changes in humidity can also affect the power consumption of sensors. For example, in high humidity environments, certain parts of the sensor may require additional energy to prevent condensation or corrosion, or humidity affects the conductivity of the sensor material, thus affecting power consumption.

[0064] In any of the above embodiments, the prediction model is specifically the following formula:

[0065] φ(B)(1-B) d Y t+1 =θ(B)e t+1 ;

[0066] Where t is the time period; Y t+1 is the observed value in the next time period, and the observed value is assigned by the rated power consumption, Y t+1 The estimated value corresponding to the environmental data is obtained by Y t The corresponding power consumption and rated power consumption are generated; B is the back-shift operator, and BY t+1 =Y t ; d is the difference order; e t+1 is a white noise sequence used to characterize random disturbances; φ(B) is an autoregressive polynomial, and φ(B)=1-φ1B-φ2B 2 -…-φ p B p; p is the order of the autoregressive term; θ(B) is the moving average polynomial, and θ(B) = 1 + θ1B + θ2B 2 +…+θ q B q ; q is the order of the moving average term.

[0067] Specifically, the estimated value of each sensor corresponding to the environmental data at the current moment is

[0068] Furthermore, for n sensors in the next time period Y t+1 The estimated value of the common corresponding environmental data is obtained by the following formula:

[0069]

[0070] Where i and j both belong to {1,...,n}, and j is a sensor of i that can generate information exchange. is the estimated value of the environmental data corresponding to sensor i at time t, a ij is a weight matrix, and represents the weight between sensor i and sensor j affected by the actual distance, represents the known random regression vector of sensor j at time t, Represents a set consisting of sensor i and sensors that can exchange information with sensor i.

[0071] In any of the above embodiments, the data analysis unit further includes:

[0072] The power consumption prediction module is used to obtain pre-processed data of multiple connected time periods including the current time period, and generate the rated power consumption of the energy storage module in the next time period through a prediction model.

[0073] The energy analysis module is used to determine whether the estimated value of the rated power consumption and environmental data in the next time period matches the working state of the sensor in the current time period, and to generate analysis data.

[0074] In this embodiment, the power prediction module predicts the power consumption of the energy storage module in future time periods by analyzing the pre-processed data in a series of time periods. This helps to effectively manage energy and ensure that sensors continue to operate without unexpected downtime due to power exhaustion; it also provides insights into power usage trends and upcoming energy demands, helping system administrators with energy scheduling and maintenance planning.

[0075] The power prediction module first collects pre-processed data for multiple connected time periods from the data interaction unit. These data include but are not limited to ambient temperature, humidity, and historical power consumption data of sensors; time series prediction models such as ARIMA (autoregressive integrated moving average model) are used, which can effectively process and predict data series that change over time; machine learning-based methods such as random forests or neural networks are used, which can learn and predict future power consumption based on historical data; the model will be trained on historical data, and its prediction accuracy will be evaluated through cross-validation and other techniques to ensure the reliability of the prediction results; based on the model output, the power prediction module generates a prediction of the rated power consumption of the energy storage module in the future time period. This information is used to adjust the working strategy of the sensor, such as adjusting the data collection frequency, entering low power mode, etc., to optimize energy use.

[0076] The energy analysis module is responsible for determining whether the working status of the sensor in the current time period matches the predicted power consumption in the future time period and the estimated value of environmental data. This function helps ensure that the sensor operates effectively within the energy supply range and avoids energy shortages or excessive consumption; based on the results of the matching analysis, analytical data is generated, which can be used to guide the energy management strategy of the sensor, such as adjusting the working mode or optimizing energy consumption.

[0077] The energy analysis module first integrates the predicted power consumption data from the power prediction module and the environmental data (such as temperature, humidity, etc.) provided by the environmental monitoring module; uses algorithms to analyze whether the current working state of the sensor (such as the sensor's activity frequency, transmission power, etc.) is compatible with the predicted power consumption and environmental conditions. This involves calculating the theoretical power consumption under the current working state and comparing it with the predicted power consumption; based on environmental conditions and predicted energy consumption, evaluating the sustainability of the current working state. If the current state may lead to excessive energy consumption or insufficient power in the future, the module will recommend adjusting the working state; based on the results of the matching analysis, the energy analysis module generates guiding analysis data to help system administrators or automatic control systems decide whether it is necessary to adjust the operating parameters of the sensor, such as reducing the frequency of data collection or switching to energy-saving mode, to optimize energy use and maintain stable operation of the system.

[0078] Specifically, the specific steps of the data analysis unit are:

[0079] Data preprocessing: cleaning and converting collected data to ensure data quality and consistency;

[0080] Battery power prediction: collect and analyze past battery power data, select a time series analysis model for training, and generate prediction results for future battery power changes; among them, there is a series of historical battery power data, recorded as E1, E2, …, E t , where E tRepresents the battery power at time t, predicting the battery power E at a future time T T ; Use the autoregressive integrated moving average model to model the time series of battery power, expressed as ARIMA(p,d,q).

[0081] Energy consumption pattern analysis: Analyze energy consumption data, extract key features, apply algorithms to identify and classify energy consumption patterns, and generate recommendations for optimizing energy management. The energy consumption pattern analysis expression is: Suppose a set of historical energy consumption data P1, P2, …, P t , represents the energy consumption at different time points t. We hope to identify different energy consumption patterns through energy consumption pattern analysis and generate optimization suggestions; data feature extraction: calculate the average energy consumption per hour The daily peak energy consumption Ppeak, or weekly energy consumption time distribution and other characteristics; Algorithm application: Select the K-means clustering algorithm to cluster the energy consumption data P1, P2, ..., P t The energy consumption patterns are converted into feature vectors, and then K-means clustering is applied to cluster similar energy consumption patterns together to form several energy consumption pattern groups.

[0082] In any of the above embodiments, the working states of all sensors in the same time period are the same, and the energy analysis module generates analysis data by the matching number of the rated power consumption in the next time period and the working state in the current time period.

[0083] In this embodiment, in the same time period, assuming that all sensors have the same working state, the energy analysis module will perform energy consumption analysis and optimization suggestions for this unified state; analyze whether the predicted power consumption in the next time period matches the current unified working state of all sensors, and generate analysis data accordingly. These analysis data reflect the energy efficiency optimization potential and adjustment needs of the overall sensor network.

[0084] First, the predicted power consumption of each sensor in the next time period is obtained from the power prediction module. These data are usually based on historical energy consumption data and model predictions of environmental factors (such as temperature and humidity); the theoretical power consumption under the current working state is evaluated. This usually involves calculating the energy consumption under a given operating frequency, transmission power and other operating parameters; calculating the theoretical total power consumption under the current unified working state and comparing it with the sum of the predicted power consumption of all sensors in the next time period. This step evaluates whether the entire sensor network can continue to operate under the current working state without causing energy shortages; according to the degree of match between the predicted power consumption of each sensor and the theoretical power consumption, the number of successful and unsuccessful matches is counted. These data help identify possible energy shortages or excess energy situations; based on the matching analysis results, analytical data is generated, such as recommended adjusted working states, energy efficiency optimization measures, etc. These data can be used to guide actual sensor network management to ensure that all sensors operate under optimal energy consumption conditions.

[0085] In any of the above embodiments, the energy optimization unit includes:

[0086] The user setting module is connected to the control end; the user setting module adjusts the judgment rule according to the instruction to adapt to the temperature and humidity of the current environment of the sensor.

[0087] The judgment control module is used to judge whether the analysis data of the current time period is qualified through the adjusted judgment rules, and generate control data through the judgment result.

[0088] In this embodiment, the user setting module is responsible for receiving instructions from the control end, which include but are not limited to environmental adaptability settings, such as adjusting the operating parameters of the sensor according to the current temperature and humidity; according to the user instructions, the module will adjust the judgment rules of the sensor to ensure that the operating configuration of the sensor is suitable for the current environmental conditions, thereby optimizing energy consumption and maintaining equipment performance.

[0089] The user setting module is connected to the control end through a communication interface, which may be network-based (such as TCP / IP protocol) or use other industrial communication standards (such as Modbus, OPC UA, etc.). This allows the module to receive setting changes made by the user on the control end; when the control end sends new setting values, the user setting module parses these inputs to determine their specific impact on the system. For example, the user may specify to reduce the data acquisition frequency of the sensor under high temperature conditions to reduce power consumption and prevent the device from overheating; the module contains a logic processing submodule that is responsible for adjusting the operating rules of the system based on the parsed parameters. This may include changing the trigger threshold of the sensor, adjusting the sleep and wake-up cycle of the sensor, or adjusting the data processing flow; the user setting module adjusts the operating settings of the sensor based on the received environmental parameters (such as temperature and humidity readings). For example, if the temperature exceeds the preset threshold, the module can automatically adjust the sensor to enter a low power mode while maintaining the necessary monitoring functions to avoid equipment damage and save energy.

[0090] The judgment control module is responsible for evaluating whether the analysis data in the current time period meets the preset quality and performance standards. This includes checking whether the data is within the allowable parameter range and whether it meets the energy efficiency requirements; based on the evaluation results of the analysis data, this module generates control data. These data are used to adjust the working state of the sensor, such as changing its operating mode, adjusting the collection frequency, or activating specific energy saving measures.

[0091] The judgment control module receives analysis data from the data analysis unit, which usually includes the energy consumption of the sensor, environmental impact assessment, etc. The logic unit inside the module parses the received data to determine whether its key indicators meet the set standards or thresholds; the module evaluates the eligibility of the data according to the judgment rules set by the user or system administrator. These rules may be based on data trend analysis, statistical anomaly detection or other quality assurance standards; if the environment changes or the system policy is updated, the judgment rules can be dynamically adjusted to better adapt to the new operating conditions; if the analysis data shows that the current operating state may lead to inefficiency or energy waste, the judgment control module will generate corresponding control instructions to optimize or adjust the operation; the control data will be sent to the relevant equipment or control system to implement the necessary adjustments.

[0092] Furthermore, the judgment rule is to adjust the data collection frequency of the sensor to adapt to the temperature and humidity of the current environment to minimize energy consumption while maintaining data quality.

[0093] condition:

[0094] If the temperature exceeds 30°C (high temperature condition), and the relative humidity is below 50% (low humidity), it is considered that the environmental conditions may accelerate the energy consumption and wear of the sensor.

[0095] If the temperature is below 10°C (low temperature condition) and the relative humidity exceeds 80% (high humidity), it is considered that the environmental conditions may reduce the response speed of the sensor and the efficiency of the battery.

[0096] In high temperature and low humidity conditions, reduce the frequency of data collection, because high temperature may affect the operating efficiency of the sensor, and low humidity reduces the risk of environmental corrosion to the sensor, allowing for less frequent monitoring. In low temperature and high humidity conditions, increase the frequency of data collection to monitor structural problems or other related risks that may be caused by cold and humidity.

[0097] In any of the above embodiments, the control module generates control data by the following formula:

[0098] M=arg min M∈1.2...N f(D);

[0099] Wherein, M is the working state, and N is the number of working states; argmin is the working state M that makes the objective function f(D) reach the minimum value; f(D) is the number of matches between the rated power consumption in the next time period and the working state in the current time period.

[0100] In this embodiment, a decision or configuration that can minimize the function is found, M is obtained from a certain range, indicating that an optimal option is selected from N possible options. f(D) is a function that evaluates the quality of each decision or state, usually depending on multiple parameters and variables, and its result represents cost, loss or other indicators that need to be minimized.

[0101] Specifically, the working state M of the decision variable is set to represent the working mode of the sensor and take one of the following values:

[0102] M=1, indicating working mode;

[0103] M=2, indicating standby mode;

[0104] M=3, indicating sleep mode.

[0105] Specifically, the communication control unit is expressed as:

[0106] Set the control variable C to represent the remote control instructions, including adjusting the working state of the sensor, obtaining real-time monitoring data, and handling abnormal situations;

[0107] Through the communication protocol P(C,S), where P represents the function of the communication protocol, C is the control instruction, S is the status information of the sensor, and the communication protocol P includes data transmission, encryption and decryption, error detection and recovery functions;

[0108] Mathematical expression summary:

[0109] C=P-1 (P(C,S))

[0110] Among them, P -1 Represents the inverse function of the communication protocol, which is used to process the data or control instructions received from the sensor.

[0111] In any of the above embodiments, the energy storage management system further includes a data storage and backup module, which is responsible for storing historical data records, including battery power changes, energy consumption statistics and system status.

[0112] The data storage and backup module expressions are:

[0113] Set storage variable D store , represents the historical data stored in the data storage and backup module. These data include battery power change E change , Energy consumption statistics stat and system status S stat ;

[0114] Then D store It is expressed as:

[0115] D store =(E change ,P stat ,S stat )

[0116] E change It is the history of battery charge changes since the last data storage point;

[0117] P stat It is the historical energy consumption statistics;

[0118] S stat It is a record of the system status, such as normal operation, maintenance records, and fault handling.

[0119] In any of the above embodiments, the energy storage management system further includes an alarm and notification module for automatically sending an alarm and notification when the sensor power level is lower than a preset threshold or the system is abnormal, so as to notify maintenance personnel to deal with the problem in a timely manner.

[0120] The expression of alarm and notification module is:

[0121] Set the alarm variable A. When the alarm condition is met, A = 1, otherwise A = 0. The alarm condition is determined by the logic judgment function B (E, S), where E is the battery power information and S is the system status information;

[0122] A=max(0,min(1,f(E,S)))

[0123] Among them, f(E,S) is a logical function. When the power E is lower than the threshold or the system state S is abnormal, f(E,S) = 1; otherwise f(E,S) = 0; A will be 1 when the conditions are met and 0 when they are not met.

[0124] Specifically, the data interaction unit may be:

[0125] The data acquisition module uses industrial IoT sensors, such as smart sensors from Honeywell, Siemens or Schneider Electric, to collect environmental and equipment status data in real time.

[0126] The data transceiver module uses wireless communication gateway equipment, such as Cisco and Huawei's industrial routers or gateways, which is responsible for receiving and sending data and supports communication protocols such as Wi-Fi, Zigbee, and LoRa.

[0127] The data analysis unit can be:

[0128] The pre-processing module uses a Raspberry Pi with sensors for preliminary data processing, or uses edge computing devices such as Intel NUC for data cleaning, formatting, and preliminary analysis.

[0129] The power consumption prediction module uses dedicated analysis software such as MATLAB or a prediction algorithm library in Python environment (such as Scikit-learn) to analyze and predict power consumption trends based on historical data.

[0130] The Energy Analysis module uses advanced energy management software, such as Schneider Electric's PowerMonitoring Expert or Siemens' Energy Manager, to analyze and optimize energy usage efficiency.

[0131] Energy optimization units can be:

[0132] The user setting module uses a PLC system such as Allen-Bradley's ControlLogix for user input and system configuration, allowing users to set operating parameters and decision rules.

[0133] The decision control module uses an advanced control system, such as Honeywell's Experion or Emerson's DeltaV system, to execute control logic based on analyzed data and user settings.

[0134] The communication control unit can be: industrial-grade communication equipment, such as Cisco's industrial Ethernet switch, to ensure stable data transmission and manage data communication between all modules in the system.

[0135] In the description of the present invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0136] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. An energy storage management system for construction safety monitoring equipment, characterized in that: include: A data interaction unit is connected to the sensor deployed in the construction site, and is used to obtain the working data of the sensor and the environmental data that affects the working state of the sensor; wherein the working data includes the monitoring data collected by the sensor at the construction site and the energy consumption data of the energy storage module inside the sensor; A data analysis unit, which acquires the working data and the environmental data and generates analysis data through a built-in prediction model; An energy optimization unit, which generates control data for adjusting the working state of the sensor according to the analysis data and a preset judgment rule; a communication control unit connected to the control end and the sensor, respectively, and sending the control data to the sensor and sending the working data, the environmental data and the control data to the control end; the control end transmits an instruction to adjust the judgment rule through the communication control unit; Wherein, the data analysis unit includes a preprocessing module; And the preprocessing module packages the environmental data and the energy consumption data into preprocessing data, specifically the following formula: D = (E, P, T, H); In the formula, D is the preprocessing data, which is used to characterize the rated power consumption of the energy storage module; E is the remaining power of the energy storage module; P is the power consumption of the energy storage module in the current time period; T is the temperature value included in the environmental data; H is the humidity value included in the environmental data; And the prediction model is specifically the following formula: φ(B)(1-B) d Y t+1 =θ(B)e t+1 ; Wherein, t is the current time period; Y t+1 is the observed value in the next time period, and the observed value is assigned by the rated power consumption, and the Y t+1 The estimated value corresponding to the environmental data is obtained by the Y t The corresponding power consumption and rated power consumption are generated; B is the back-shift operator, and BY t+1 =Y t ; d is the differential order; e t+1 is a white noise sequence used to characterize random disturbances; φ(B) is an autoregressive polynomial, and φ(B)=1-φ1B-φ2B 2 -…-φ p B p ; p is the order of the autoregressive term; θ(B) is a moving average polynomial, and θ(B) = 1 + θ1B + θ2B 2 +…+θ q B q ; The q is the order of the moving average term; And for n sensors in the next time period Y t+1 The estimated value of the common corresponding environmental data is obtained by the following formula: Where i and j belong to {1,...,n}, and j is the sensor of i that can generate information exchange, is the estimated value of the environmental data corresponding to sensor i in the time period t+1, a ij is a weight matrix, and represents the weight between sensor i and sensor j affected by the actual distance, represents the known random regression vector of sensor j at time t, Represents a set consisting of sensor i and sensors that can exchange information with sensor i.

2. The energy storage management system according to claim 1, characterized in that: The data exchange unit is connected to at least one of the n sensors via a wireless network, and the sensors exchange data with each other via a wired network and / or a wireless network.

3. The energy storage management system according to claim 2, characterized in that: The data interaction unit comprises: A data acquisition module, used to obtain the environmental data and the monitoring data; The data transceiver module is used to receive the energy consumption data and send control data to the sensor via a wired network and / or a wireless network.

4. The energy storage management system according to claim 1, characterized in that: The power consumption is a value of the rated power consumption of the sensor in the current working state after being affected by the temperature value and the humidity value in the environmental data.

5. The energy storage management system according to claim 1, characterized in that: The data analysis unit also includes: An electricity consumption prediction module, used to obtain preprocessed data of a plurality of connected time periods including the current time period, and generate a rated power consumption of the energy storage module in the next time period through the prediction model; The energy analysis module is used to determine whether the rated power consumption in the next time period and the estimated value of the environmental data match the working state of the sensor in the current time period, and to generate the analysis data.

6. The energy storage management system according to claim 5, characterized in that: The working states of all the sensors are the same in the same time period, and the energy analysis module generates the analysis data by the matching number of the rated power consumption in the next time period and the working state in the current time period.

7. The energy storage management system according to claim 6, characterized in that: The energy optimization unit comprises: A user setting module connected to the control end; the user setting module adjusts the judgment rule according to the instruction to adapt to the temperature and humidity of the environment where the sensor is currently located; The judgment control module is used to judge whether the analysis data of the current time period is qualified according to the adjusted judgment rule, and generate the control data according to the judgment result.

8. The energy storage management system according to claim 7, characterized in that: The judgment control module generates the control data through the following formula: Among them, M is the working state, and N is the number of working states; arg min is the working state M that makes the objective function f(D) reach the minimum value; f(D) is the matching number of the rated power consumption in the next time period and the working state in the current time period.

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