A big data-based intelligent alarm early warning system and method

Abstract

The present invention discloses a big data-based intelligent alarm early warning system and method, comprising a data acquisition module, a data storage module, a model building module, a comparison and perception module, a comprehensive analysis module, and an environmental matching module. The data acquisition module collects prefabricated house load information and prefabricated house attribute information. The model building module uses the prefabricated house load information and prefabricated house attribute information to establish a perception model and generate a plate material evaluation index. The comparison and perception module compares the generated plate material evaluation index with a plate material evaluation threshold to generate high-risk and low-risk plate material signals. The environmental matching module generates different types of alarm processing signals based on the comparison results of the high- and low-risk plate material signals and the environmental floating threshold and environmental floating coefficient. The present invention comprehensively analyzes the status of prefabricated houses, thereby realizing alarm early warning, and carrying out maintenance of prefabricated houses in advance based on different information, thereby reducing the accident rate.

Description

Technical Field

[0001] The present invention relates to the field of alarm early warning technology, and in particular to an intelligent alarm early warning system and method based on big data. Background Art

[0002] Big data refers to a large, diverse and rapidly growing collection of data. It is characterized by high speed, high volume, high diversity and high value density. The value of big data is mainly reflected in its ability to analyze and mine information. Through the collection, storage, processing and analysis of big data, hidden patterns, trends and associations can be revealed, thereby providing deeper insights and more accurate predictions for decision-making.

[0003] A smart alarm is a security device that integrates intelligent technology and alarm functions. It can sense abnormal conditions in the environment through various sensors and monitoring devices installed in the house, and when potential dangers or emergencies are detected, it will alert users or relevant personnel through alarms, notifications or linkage with other devices.

[0004] The big data-based intelligent alarm warning system is a system that uses big data technology and intelligent algorithms to provide security warnings and alarms. It collects, stores and analyzes large amounts of data, including sensor data, monitoring data, user behavior data, etc., to identify abnormal events and potential risks, and trigger alarm mechanisms in a timely manner to provide effective security protection.

[0005] The existing technology has the following deficiencies: the existing technology only installs multiple smart alarm devices on immovable fixed houses to build a home smart alarm early warning system, thereby performing early warning monitoring on fixed houses, but there is no specific intelligent early warning system for simple houses or temporary buildings, such as mobile houses, which are temporary residential structures that can be quickly built and moved. Due to the characteristics of mobile panels that can be used multiple times, wear and tear will be caused on the joints of the panels of the mobile houses, and the sealing of the joints and the condition of the panel surface of the mobile panels will have a crucial impact on the stability of the entire mobile house. However, there is serious uncertainty in the early warning of the panel joints and the movable panel surface of the mobile house, so it is impossible to perform structured early warning monitoring of the mobile house, and the status of the mobile house is easily affected by the surrounding environment. There is a lack of analysis between the historical surrounding environment and the status of the mobile house, which can easily lead to incorrect judgment of the status of the mobile house.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide an intelligent alarm warning system and method based on big data. The present invention obtains different processing signals by comprehensively analyzing the status of the movable panel and the historical surrounding environment, and then adopts targeted processing measures to process the movable panel house according to different types of processing signals, thereby improving the warning processing efficiency.

[0008] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent alarm warning system based on big data, comprising a data acquisition module, a data storage module, a model building module, a comparison perception module, a comprehensive analysis module and an environment matching module;

[0009] The data acquisition module collects information about the prefabricated house, including the load information and property information of the prefabricated house, and collects historical environmental monitoring data information. After the collection, the load information, property information and historical environmental monitoring data information are transmitted to the data storage module;

[0010] The data storage module stores the data collected by the data acquisition module, stores threshold information, historical environmental monitoring data information, and sends the data to the model building module;

[0011] The model building module generates a panel evaluation index based on the panel load information and panel property information, and transmits the panel evaluation index to the comparison perception module;

[0012] The comparison perception module receives the data sent by the model building module, compares the generated plate evaluation index with the plate evaluation threshold, generates high-risk plate house signals and low-risk plate house signals, and transmits the generated signals to the comprehensive analysis module;

[0013] The comprehensive analysis module, after receiving the signal sent by the comparative perception module, obtains the historical environmental monitoring data information of the prefabricated house during the monitoring period, generates the environmental floating coefficient based on the historical environmental monitoring data information, and sends the data to the environmental matching module;

[0014] The environment matching module receives the data sent by the comprehensive analysis module, performs matching analysis on the surrounding environment of the prefabricated house and the state of the prefabricated house, and generates different types of alarm processing signals.

[0015] Preferably, the load information of the board room includes the air pressure duration value and the vibration filter coefficient. After collection, the data acquisition module calibrates the air pressure duration value and the vibration filter coefficient as QY i and ZD i , the board room attribute information includes the board surface concave and convex ratio value. After collection, the data acquisition module calibrates the board surface concave and convex ratio value as BM i .

[0016] Preferably, the logic for obtaining the air pressure duration value is as follows:

[0017] Differential pressure sensors are installed at the joints of the prefabricated house to measure the air pressure on both sides of the joints;

[0018] Differential pressure sensor A is located indoors, and differential pressure sensor B is located outdoors;

[0019] Obtain the air pressure values in differential pressure sensor A and differential pressure sensor B, calibrate the air pressures of differential pressure sensor A and differential pressure sensor B as AM and BM respectively, and record the duration T of the real-time air pressure difference;

[0020] The air pressure duration value is calculated using the formula:

[0021] QY i =(AM-BM)*T.

[0022] Preferably, the logic for obtaining the vibration filter coefficient is as follows:

[0023] The peak value of the vibration signal is the maximum amplitude value Xmax in the signal waveform, the peak value of the vibration signal is the minimum amplitude value Xmin in the signal waveform, and the average amplitude value of the sampling sequence is recorded as Xavg. The calculation formula of the vibration filter coefficient is as follows: ZD i =(Xmax-Xmi n) / Xavg.

[0024] Preferably, the logic for obtaining the board surface concave-convex ratio value is as follows:

[0025] Import the taken photos into image processing software, mark the boundaries of the concave or convex areas on the image, draw the boundary lines, measure the distance between the boundary lines according to the reference scale on the image, record the measured concave and convex degree data, and take the ratio of the surface area of the concave and convex areas on the board surface to the overall board surface area as the board surface concave and convex ratio value.

[0026] Preferably, the generated board evaluation index is compared with the board evaluation threshold to generate a high-risk board house signal and a low-risk board house signal. The specific process is as follows:

[0027] If the plate evaluation index is greater than the plate evaluation threshold, a high-risk plate house signal is generated through the comparative perception module. If the plate evaluation index is less than or equal to the plate evaluation threshold, a low-risk plate house signal is generated through the comparative perception module and the signal is transmitted to the comprehensive analysis module.

[0028] Preferably, the environmental floating coefficient is generated based on historical environmental monitoring data information, and the specific process is as follows:

[0029] Obtain the average of the maximum and minimum wind strength ranges outside the prefabricated house and mark it as the wind standard value, and mark the absolute value of the difference between the wind strength value and the wind standard value as the wind pressure floating value;

[0030] Obtain the external air humidity value and humidity range of the monitored prefabricated house, mark the average of the maximum and minimum values of the humidity range as the humidity standard value, and mark the absolute value of the difference between the air humidity value and the humidity standard value as the humidity floating value;

[0031] The sum of the wind pressure floating value multiplied by the wind pressure floating value weight and the humidity floating value multiplied by the humidity floating value weight is taken as the environmental floating coefficient.

[0032] Preferably, the surrounding environment of the prefabricated house and the state of the prefabricated house are matched and analyzed to generate different types of alarm processing signals. The specific process is as follows:

[0033] If the environmental fluctuation coefficient is less than the environmental fluctuation threshold and the comprehensive analysis module receives a low-risk prefabricated house signal, it is determined that the historical environment of the monitored prefabricated house matches the status of the prefabricated board. The environmental matching module sends a matching qualified signal to the intelligent alarm, and the intelligent alarm does not issue an early warning.

[0034] If the environmental fluctuation coefficient is less than the environmental fluctuation threshold and the comprehensive analysis module receives a high-risk prefabricated house signal, it is determined that the historical environmental monitoring data information of the monitored prefabricated house does not match the prefabricated house status, and the environmental matching module sends an abnormal prefabricated house signal to the intelligent alarm;

[0035] If the environmental fluctuation coefficient is greater than or equal to the historical threshold and the comprehensive analysis module receives a low-risk prefabricated house signal, it is determined that the monitored historical environmental monitoring data information does not match the status of the prefabricated house, and the environmental matching module sends an abnormal environment signal to the intelligent alarm;

[0036] If the environmental floating coefficient is greater than or equal to the environmental floating threshold and the comprehensive analysis module receives a high-risk prefabricated house signal, it is determined that the historical environmental monitoring data information of the monitored prefabricated house does not match the status of the prefabricated house, and the environmental matching module sends a danger warning signal to the intelligent alarm.

[0037] A big data-based intelligent alarm early warning method includes the following steps:

[0038] Collect prefabricated house information, including prefabricated house load information and prefabricated house attribute information, and collect historical environmental monitoring data information;

[0039] Establish a perception model based on the load information and property information of the panel house to generate a panel evaluation index;

[0040] Compare the generated panel evaluation index with the panel evaluation threshold to generate high-risk panel house signals and low-risk panel house signals;

[0041] Obtain the plate evaluation index and the generated signal at the corresponding moment, and conduct a comprehensive analysis of historical environmental monitoring data information to obtain the environmental fluctuation coefficient;

[0042] Different types of alarm processing signals are generated based on the comparison results of high-risk and low-risk board room signals and environmental floating thresholds and environmental floating coefficients.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] The present invention generates a plate evaluation index by collecting plate house load information and plate house properties, generates high-risk plate house signals and low-risk plate house signals through the plate evaluation index, and compares the plate evaluation threshold and the environmental floating threshold with the plate evaluation index and the environmental floating coefficient respectively, thereby matching and analyzing the historical environmental monitoring data information of the portable plate house with the portable plate house status. By comprehensively analyzing the numerical values of the historical coefficient and the plate evaluation index, the alarm generates different types of processing signals, and analyzes the proportions of different types of processing signals, thereby adopting targeted processing measures to process the portable plate house according to the proportions of different types of processing signals, thereby improving the efficiency of early warning processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 This is a module diagram of a big data-based intelligent alarm early warning system and method of the present invention.

[0047] Figure 2 This is a flow chart of a method of an intelligent alarm early warning system and method based on big data of the present invention. DETAILED DESCRIPTION

[0048] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0049] Example 1: The present invention provides Figure 1 The intelligent alarm warning system based on big data shown includes a data acquisition module, a data storage module, a comprehensive analysis module and an environment matching module;

[0050] The data acquisition module collects information about the prefabricated house, including load information and property information of the prefabricated house. After the information is collected, the load information and property information of the prefabricated house are transmitted to the model building module.

[0051] The data storage module stores the data information collected by the data acquisition module, and stores the used threshold information and historical data information;

[0052] Prefabricated houses are used in a variety of different scenarios. As a movable and detachable building structure, prefabricated houses can meet temporary needs and can be adjusted and moved according to actual conditions. Compared with traditional brick-concrete structures or steel structures, prefabricated houses are faster to build. Through prefabricated panels and modular design, the required space can be built in a shorter time, which improves construction efficiency. Prefabricated houses can be disassembled and reassembled, which is convenient for adjustment and movement at different stages. Compared with traditional building structures, prefabricated houses have lower costs, their material costs are lower, and they save time and labor costs during the construction process. For temporary construction needs, prefabricated houses are an economical and practical choice.

[0053] However, during the disassembly and assembly process of the prefabricated house, since the movable panels need to be connected, the quality and sealing of the joints are crucial to the performance and use of the prefabricated house, because it determines whether the connection between the panels is firm and sealed. Different methods and materials are often used to treat the joints to ensure that the connection between the panels is firm and prevent problems such as water leakage and dust.

[0054] The load information of the board room includes the air pressure duration value and the vibration filter coefficient. After collection, the data acquisition module calibrates the air pressure duration value and the vibration filter coefficient as QY i and ZD i ;

[0055] The air pressure duration at the joints of movable panels has a significant impact on the safety of prefabricated houses. The air pressure duration is an indicator used to measure the degree of balance between the internal and external air pressures at the joints of movable panels. A higher air pressure duration indicates a larger imbalance in the internal and external air pressures. That is, when there is a large air pressure difference at the joints of prefabricated houses, the following effects may occur:

[0056] Air and water leakage: Large pressure differences may reduce the sealing of joints, resulting in air or water leakage, which will lead to unstable indoor temperature, energy waste, and deterioration of the indoor environment;

[0057] Difficulty in temperature regulation: The existence of air pressure differences may make it difficult to regulate the temperature inside the prefabricated house. For example, if cold air flows into the joints, the heater may have difficulty keeping the room warm, thus affecting comfort and energy efficiency.

[0058] Structural safety hazards: A large pressure difference at the joints may affect the structural stability of the prefabricated house. Excessive pressure difference may increase the stress at the joints, causing damage, loosening or deformation at the joints, thereby affecting the overall structural safety;

[0059] Energy waste: Air or water leaks waste energy because hot and cold air in the room escapes, requiring additional energy to maintain a balanced indoor temperature, which increases energy costs and negatively impacts sustainability.

[0060] The logic for obtaining the air pressure duration value is as follows:

[0061] Two differential pressure sensors are installed at the joints of the prefabricated house to measure the air pressure on both sides of the joints;

[0062] Differential pressure sensor A is located indoors, and differential pressure sensor B is located outdoors;

[0063] Obtain the air pressure values in differential pressure sensor A and differential pressure sensor B, calibrate the air pressures of differential pressure sensor A and differential pressure sensor B as AM and BM respectively, and record the duration T of the real-time air pressure difference;

[0064] The air pressure duration value is calculated by the formula based on:

[0065] Air pressure duration value = (AM-BM)*T;

[0066] Get the air pressure duration value QY i ;

[0067] It should be noted that a suitable air pressure sensor, such as a differential pressure sensor or an absolute pressure sensor, is selected. The differential pressure sensor is suitable for measuring the difference in air pressure between the joint and the surrounding air, while the absolute pressure sensor can be used to measure the absolute air pressure value at the joint. The differential pressure sensor is installed close to the joint to ensure that the air pressure changes at the joint can be accurately sensed. The sensor has an electrical connection interface and can be connected to the monitoring system through a cable or wire. The air pressure time value of the movable plate at the joint is analyzed overall, and the air pressure value of each internal differential pressure sensor and the external differential pressure sensor are used to obtain the air pressure value;

[0068] The threshold information includes air pressure duration threshold, vibration threshold, concave-convex area threshold, plate evaluation threshold and other threshold information;

[0069] Prefabricated houses are placed near the construction area and are easily affected by the vibrations generated by large-scale machinery and equipment in the construction area. The high frequency and amplitude of vibrations can easily have an adverse effect on prefabricated houses.

[0070] Vibration filter coefficient ZD i, refers to the degree of external vibration in the construction area of the prefabricated house. When the vibration intensity and frequency borne by the joints of the prefabricated house are too high, problems are likely to occur at the joints;

[0071] The vibration filter coefficient at the joints of the prefabricated house has a certain impact on the safety of the prefabricated house. When there are many vibrations around the prefabricated house, the following problems may occur at the joints:

[0072] Loose joints: Strong vibrations may cause the joints of prefabricated houses to loosen. Continuous vibrations may gradually loosen the fasteners and connectors at the joints, thereby affecting the sealing and stability of the joints.

[0073] Joint damage: Vibration will generate impact force, which will put additional stress on the joints. If the joints do not have sufficient strength and durability, they may be cracked, broken or damaged, which will damage the overall structure of the board house.

[0074] Noise and discomfort: Strong vibrations can cause resonance and vibration in the prefabricated house structure, resulting in noise and discomfort, which can adversely affect the comfort and working environment inside the prefabricated house, and affect the work efficiency and health of personnel;

[0075] For example, vibration sensors detected high ground vibration frequency and amplitude during drilling operations. Data analysis revealed that the vibration frequency exceeded the prescribed limit, which could mean that the drilling machine generated strong vibration forces during operation, potentially adversely affecting nearby prefabricated houses.

[0076] Therefore, obtaining the vibration filter coefficient of the prefabricated house can be used to analyze the overall state of the prefabricated house;

[0077] Vibration filter coefficient ZD i The acquisition logic is as follows:

[0078] Install the vibration sensor at the location where vibration needs to be monitored, such as on the ground or structure near the prefabricated house. The vibration sensor will continuously collect signal data of ground vibration, calculate the amplitude of the vibration signal, and obtain the amplitude information of the vibration. The calculated or adjusted vibration amplitude is recorded to represent the amplitude characteristics of the ground vibration. The peak value of the vibration signal is the maximum amplitude value Xmax in the signal waveform, and the peak value of the vibration signal is the minimum amplitude value Xmin in the signal waveform. The average amplitude value Xavg of the sampling sequence is recorded. The vibration filter coefficient is calculated as follows: ZD i =(Xmax-Xmin) / Xavg;

[0079] It should be noted that special vibration measuring sensors, such as vibration sensors, are installed at key locations of the prefabricated house, such as supporting structures and panel joints. The vibration sensors can monitor the frequency, amplitude and direction of vibration in real time and transmit the data to the monitoring system for analysis and recording, thereby inferring the impact of vibration on the prefabricated house.

[0080] The property information of the prefabricated house includes the ratio of the concave and convex surface of the board surface. After collection, the data acquisition module calibrates the ratio of the concave and convex surface of the board surface as BM i ;

[0081] When the movable board is subjected to external pressure, such as the weight of people or objects, the pressure of stacked items, etc., it may cause the board surface to become concave. If the support points of the movable board are uneven or unstable, the force on the board surface will be uneven, resulting in concave and convex changes. The movable board surface will also become concave and convex due to aging caused by long-term use.

[0082] When the surface convexity of the movable board is too large, it will affect the use of the movable board house. The following are the aspects that have an impact:

[0083] Weakened structural strength: Concave and convex surfaces may affect the structural strength of the board. The concave parts may become stress concentration areas, increasing the stress and deformation of the board, which may make the board more susceptible to damage or breakage when subjected to wind, vibration or other external forces;

[0084] Affecting coating adhesion: If there are bumps on the surface of the plate, the coating (such as paint, anti-corrosion coating, etc.) may not adhere evenly to the surface, reducing the adhesion and durability of the coating;

[0085] Impact on interior decoration and equipment installation: Uneven surfaces may make it difficult to install interior decoration materials and equipment. For example, if the surface of the board is uneven, it may be difficult to install wall materials, floor materials or equipment, which may affect the decorative effect and usage function.

[0086] Therefore, by obtaining the concave-convex ratio of the movable plate, the movable plate can be intelligently analyzed;

[0087] The logic for obtaining the board surface concave-convex ratio value is as follows:

[0088] Import the captured photos into image processing software, select a measurement tool in the image processing software, such as a straight line measurement tool, mark the boundaries of the concave or convex areas on the image, draw boundary lines, measure the distance between the boundary lines according to the reference scale on the image, record the measured concave and convex degree data, and use the ratio of the surface area of the concave and convex areas on the board surface to the overall board surface area as the board surface concave and convex ratio value;

[0089] For example, if the depth of the concave surface is 8 mm, the concave surface area is 0.11 square meters, the height of the protruding surface is 5 mm, the protruding surface area is 0.1 square meters, and the movable surface area is 21 square meters, then the concave-convex ratio of the board surface is 0.01;

[0090] It should be noted that the convex-concave ratio of the panel surface in the prefabricated house can be obtained by taking pictures of the panel surface. The camera equipment can be installed on the prefabricated panel surface to capture the panel surface information in a covering camera manner, thereby obtaining the real-time value of the convex-concave ratio of the panel surface.

[0091] The model building module uses the panel load information and panel property information to establish a panel evaluation index, and transmits the panel evaluation index to the comparison perception module;

[0092] The model building module obtains the air pressure duration value QY i , vibration filter coefficient ZD i , board surface concave and convex ratio BM i After that, a perception model is established to generate a plate evaluation index, which is calibrated as BG based on the following formula:

[0093] Where α, β, and γ are the air pressure duration values QY i , vibration filter coefficient ZD i , board surface concave and convex ratio BM i The preset proportional coefficient, and α, β, and γ are all greater than 0;

[0094] The formula shows that the larger the air pressure duration value, the larger the vibration filter coefficient, and the larger the proportion of unevenness on the board surface, that is, the larger the performance value of the board evaluation index BG, the higher the probability of abnormality in the prefabricated house. The smaller the air pressure duration value, the smaller the vibration filter coefficient, and the smaller the proportion of unevenness on the board surface, that is, the smaller the performance value of the board evaluation index BG, the lower the probability of abnormality in the prefabricated house.

[0095] The comparative perception module compares the generated panel evaluation index with the panel evaluation threshold, generates high-risk panel house signals and low-risk panel house signals, and transmits the generated signals to the comprehensive analysis module;

[0096] After the comparative perception module obtains the plate evaluation index generated by the prefabricated house, it compares the generated plate evaluation index with the plate evaluation threshold. If the plate evaluation index is greater than the plate evaluation threshold, indicating that the probability of abnormality of the plate of the prefabricated house is high, a high-risk plate house signal is generated by the comparative perception module and the high-risk plate house signal is transmitted to the comprehensive analysis module. If the plate evaluation index is less than or equal to the plate evaluation threshold, indicating that the probability of abnormality of the prefabricated house is low, a low-risk plate house signal is generated by the comparative perception module and the low-risk plate house signal is transmitted to the comprehensive analysis module.

[0097] The comprehensive analysis module, after receiving the risky prefabricated house signal sent by the comparative perception module, obtains the plate evaluation index and the generated environmental floating coefficient generated at the corresponding moment, and conducts a comprehensive assessment of the prefabricated house;

[0098] Before building the prefabricated house, analyze the surrounding environment of the prefabricated house, build the prefabricated house after meeting the environmental requirements, and continuously monitor the surrounding environment of the prefabricated house after building;

[0099] The comprehensive analysis module monitors and analyzes the surrounding environment of the prefabricated house and divides the monitoring cycle into two parts. The monitoring period is selected according to the actual situation, and the prefabricated house is marked as the monitoring object. During the monitoring period, the wind pressure fluctuation value FY and humidity fluctuation value SD in the historical environmental monitoring data of the prefabricated house are obtained.

[0100] The wind pressure floating value FY is obtained by monitoring the external wind intensity value and the external wind intensity range of the prefabricated house, obtaining it through a wind speed sensor, marking the average of the maximum and minimum values of the wind intensity range as the wind standard value, and marking the absolute value of the difference between the wind intensity value and the wind standard value as the wind pressure floating value FY. The humidity floating value SD is obtained by monitoring the external air humidity value and the humidity range of the prefabricated house, marking the average of the maximum and minimum values of the humidity range as the humidity standard value, and marking the absolute value of the difference between the air humidity value and the humidity standard value as the humidity floating value SD.

[0101] The environmental floating coefficient FD of the monitored object is obtained through the formula FD = δ*FY + θ*SD;

[0102] The environmental floating coefficient is a value that reflects the normality of the external environment of the prefabricated house. The smaller the value of the environmental floating coefficient, the higher the normality of the external environment of the prefabricated house. δ and θ are preset proportional coefficients.

[0103] Obtain the historical maximum environmental fluctuation coefficient FDmax in the data storage module before the prefabricated house is built, and store it in the data storage module as the environmental fluctuation threshold;

[0104] The environmental matching module is used to perform a matching analysis between historical environmental monitoring data and the status of the prefabricated housing. The environmental floating threshold is obtained through the data storage module, and the environmental floating threshold and environmental floating coefficient are comprehensively analyzed with the low-risk prefabricated housing signals and high-risk prefabricated housing signals received by the comprehensive analysis module.

[0105] If the environmental fluctuation coefficient is less than the environmental fluctuation threshold and the comprehensive analysis module receives a low-risk prefabricated house signal, it is determined that the monitored prefabricated house environment matches the prefabricated board status. The environmental matching module sends a matching qualified signal to the intelligent alarm, and the intelligent alarm does not issue an early warning.

[0106] If the environmental fluctuation coefficient is less than the environmental fluctuation threshold and the comprehensive analysis module receives a high-risk prefabricated house signal, it is determined that the historical environmental monitoring data information of the monitored prefabricated house does not match the prefabricated house status, and a problem has occurred in the prefabricated house during the construction or living process. The environmental matching module sends an abnormal prefabricated house signal to the smart alarm, notifying maintenance personnel to maintain and inspect the prefabricated house;

[0107] If the environmental fluctuation coefficient is greater than or equal to the historical threshold and the comprehensive analysis module receives a low-risk prefabricated house signal, it is determined that the monitored historical environmental monitoring data information does not match the status of the prefabricated house. The environmental matching module sends an abnormal environmental signal to the intelligent alarm, indicating that the environment after the prefabricated house is built is significantly different from the environment before the prefabricated house is built, but the impact on the prefabricated house is small;

[0108] If the environmental fluctuation coefficient is greater than or equal to the environmental fluctuation threshold and the comprehensive analysis module receives a high-risk prefabricated house signal, it is determined that the historical environmental monitoring data information of the monitored prefabricated house does not match the state of the prefabricated house. The environmental matching module sends a danger warning signal to the intelligent alarm, indicating that the environment after the prefabricated house is built does not match the historical environment. The prefabricated house is in poor condition and prone to accidents. The maintenance personnel are notified to inspect the prefabricated house or change the construction site.

[0109] The historical environmental monitoring data information of the prefabricated house is matched and analyzed with the status of the prefabricated house. By analyzing the environmental floating coefficient and the different risk signals of the prefabricated house during the monitoring period, the alarm generates different types of processing signals, and analyzes the proportion of different types of processing signals. Therefore, targeted processing measures are adopted to process the prefabricated house according to the proportion of different types of processing signals, thereby improving the efficiency of early warning processing.

[0110] The present invention generates a plate evaluation index by collecting plate house load information and plate house properties, generates high-risk plate house signals and low-risk plate house signals through the plate evaluation index, and compares the plate evaluation threshold and the environmental floating threshold with the plate evaluation index and the environmental floating coefficient respectively, thereby matching and analyzing the historical environmental monitoring data information of the portable plate house with the portable plate house status. By comprehensively analyzing the numerical values of the historical coefficient and the plate evaluation index, the alarm generates different types of processing signals, and analyzes the proportions of different types of processing signals, thereby adopting targeted processing measures to process the portable plate house according to the proportions of different types of processing signals, thereby improving the efficiency of early warning processing.

[0111] The present invention provides Figure 2 The big data-based intelligent alarm early warning method shown includes the following steps:

[0112] Collect prefabricated house information, including prefabricated house load information and prefabricated house attribute information, and collect historical environmental monitoring data information;

[0113] Establish a perception model based on the load information and property information of the panel house to generate a panel evaluation index;

[0114] Compare the generated panel evaluation index with the panel evaluation threshold to generate high-risk panel house signals and low-risk panel house signals;

[0115] Obtain the plate evaluation index and the generated signal at the corresponding moment, and conduct a comprehensive analysis of historical environmental monitoring data information to obtain the environmental fluctuation coefficient;

[0116] Different types of alarm processing signals are generated based on the comparison results of high-risk and low-risk board room signals and environmental floating thresholds and environmental floating coefficients.

[0117] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0118] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0119] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0120] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0121] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0123] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0125] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An intelligent alarm warning system based on big data, characterized in that: It includes data acquisition module, data storage module, model building module, comparison perception module, comprehensive analysis module and environment matching module; The data acquisition module collects information about the prefabricated house, including load information and property information of the prefabricated house, and collects historical environmental monitoring data information. The load information of the prefabricated house includes the air pressure duration value and the vibration filter coefficient, and the property information of the prefabricated house includes the ratio of the concave and convex parts of the board surface. After collection, the load information, property information and historical environmental monitoring data of the prefabricated house are transmitted to the data storage module; The data storage module stores the data collected by the data acquisition module, stores threshold information, historical environmental monitoring data information, and sends the data to the model building module; The model building module generates a panel evaluation index based on the panel load information and panel property information, and transmits the panel evaluation index to the comparison perception module; The model building module obtains the air pressure duration value , vibration filter coefficient , the ratio of concave and convex surface After that, the perception model is established to generate the plate evaluation index, and the plate evaluation index is calibrated as , based on the formula: Where, 、 、 Is the air pressure duration value , vibration filter coefficient , the ratio of concave and convex surface The preset scaling factor of 、 、 All greater than 0; The comparison perception module receives the data sent by the model building module, compares the generated plate evaluation index with the plate evaluation threshold, generates high-risk plate house signals and low-risk plate house signals, and transmits the generated signals to the comprehensive analysis module; The comprehensive analysis module, after receiving the signal sent by the comparative perception module, obtains the historical environmental monitoring data information of the prefabricated house during the monitoring period, generates the environmental floating coefficient based on the historical environmental monitoring data information, and sends the data to the environmental matching module; The environment matching module receives the data sent by the comprehensive analysis module, performs matching analysis on the surrounding environment of the prefabricated house and the state of the prefabricated house, and generates different types of alarm processing signals.

2. The intelligent alarm warning system based on big data according to claim 1 is characterized in that: The logic for obtaining the air pressure duration value is as follows: Differential pressure sensors are installed at the joints of the prefabricated house to measure the air pressure on both sides of the joints; Differential pressure sensor A is located indoors, and differential pressure sensor B is located outdoors; Obtain the air pressure values in differential pressure sensor A and differential pressure sensor B, calibrate the air pressures of differential pressure sensor A and differential pressure sensor B as AM and BM respectively, and record the duration T of the real-time air pressure difference; The air pressure duration value is calculated by the formula based on: 。 3. The intelligent alarm warning system based on big data according to claim 1 is characterized in that: The logic for obtaining the vibration filter coefficient is as follows: The peak value of the vibration signal is the maximum amplitude value Xmax in the signal waveform, the peak value of the vibration signal is the minimum amplitude value Xmin in the signal waveform, and the average amplitude value of the sampling sequence is recorded. , the calculation formula of the vibration filter coefficient is as follows: .

4. The intelligent alarm warning system based on big data according to claim 1 is characterized in that: The logic for obtaining the board surface concave-convex ratio value is as follows: Import the taken photos into image processing software, mark the boundaries of the concave or convex areas on the image, draw the boundary lines, measure the distance between the boundary lines according to the reference scale on the image, record the measured concave and convex degree data, and take the ratio of the surface area of the concave and convex areas on the board surface to the overall board surface area as the board surface concave and convex ratio value.

5. The intelligent alarm warning system based on big data according to claim 4 is characterized in that: The generated panel evaluation index is compared with the panel evaluation threshold to generate high-risk panel house signals and low-risk panel house signals. The specific process is as follows: If the plate evaluation index is greater than the plate evaluation threshold, a high-risk plate house signal is generated through the comparative perception module. If the plate evaluation index is less than or equal to the plate evaluation threshold, a low-risk plate house signal is generated through the comparative perception module and the signal is transmitted to the comprehensive analysis module.

6. The intelligent alarm warning system based on big data according to claim 5 is characterized in that: The environmental floating coefficient is generated based on historical environmental monitoring data. The specific process is as follows: Obtain the average of the maximum and minimum wind strength ranges outside the prefabricated house and mark it as the wind standard value, and mark the absolute value of the difference between the wind strength value and the wind standard value as the wind pressure floating value; Obtain the external air humidity value and humidity range of the monitored prefabricated house, mark the average of the maximum and minimum values of the humidity range as the humidity standard value, and mark the absolute value of the difference between the air humidity value and the humidity standard value as the humidity floating value; The sum of the wind pressure floating value multiplied by the wind pressure floating value weight and the humidity floating value multiplied by the humidity floating value weight is taken as the environmental floating coefficient.

7. The intelligent alarm warning system based on big data according to claim 6 is characterized in that: Match and analyze the surrounding environment of the prefabricated house with the state of the prefabricated house to generate different types of alarm processing signals. The specific process is as follows: If the environmental fluctuation coefficient is less than the environmental fluctuation threshold and the comprehensive analysis module receives a low-risk prefabricated house signal, it is determined that the historical environment of the monitored prefabricated house matches the status of the prefabricated board. The environmental matching module sends a matching qualified signal to the intelligent alarm, and the intelligent alarm does not issue an early warning. If the environmental fluctuation coefficient is less than the environmental fluctuation threshold and the comprehensive analysis module receives a high-risk prefabricated house signal, it is determined that the historical environmental monitoring data information of the monitored prefabricated house does not match the prefabricated house status, and the environmental matching module sends an abnormal prefabricated house signal to the intelligent alarm; If the environmental fluctuation coefficient is greater than or equal to the historical threshold and the comprehensive analysis module receives a low-risk prefabricated house signal, it is determined that the monitored historical environmental monitoring data information does not match the status of the prefabricated house, and the environmental matching module sends an abnormal environment signal to the intelligent alarm; If the environmental floating coefficient is greater than or equal to the environmental floating threshold and the comprehensive analysis module receives a high-risk prefabricated house signal, it is determined that the historical environmental monitoring data information of the monitored prefabricated house does not match the status of the prefabricated house, and the environmental matching module sends a danger warning signal to the intelligent alarm.

8. A big data-based intelligent alarm warning method, based on the big data-based intelligent alarm warning system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Collect prefabricated house information, including prefabricated house load information and prefabricated house attribute information, and collect historical environmental monitoring data information; Establish a perception model based on the load information and property information of the panel house to generate a panel evaluation index; Compare the generated panel evaluation index with the panel evaluation threshold to generate high-risk panel house signals and low-risk panel house signals; Obtain the plate evaluation index and the generated signal at the corresponding moment, and conduct a comprehensive analysis of historical environmental monitoring data information to obtain the environmental fluctuation coefficient; Different types of alarm processing signals are generated based on the comparison results of high-risk and low-risk board room signals and environmental floating thresholds and environmental floating coefficients.

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