CN118762491B

CN118762491B - Intelligent park fire alarm method, system, equipment and storage medium

Info

Publication number: CN118762491B
Application number: CN202411223123.1A
Authority: CN
Inventors: 胡宪华; 朱东生; 王彦淞; 谭炜; 李志杰
Original assignee: Guangdong Yuanpeng Network Technology Co ltd
Current assignee: Guangdong Yuanpeng Network Technology Co ltd
Priority date: 2024-09-03
Filing date: 2024-09-03
Publication date: 2024-11-26
Anticipated expiration: 2044-09-03
Also published as: CN118762491A

Abstract

The application provides a fire alarm method, a fire alarm system, fire alarm equipment and a fire alarm storage medium for an intelligent park, which are characterized in that environmental temperature data of all monitoring positions are collected; further obtaining a plurality of temperature credible domains, further determining the temperature diffusion trend of each monitoring position, and further determining the critical alarm value of the environmental temperature at each monitoring position; acquiring temperature interference records, further determining the transient fluctuation amount of the environmental temperature at each monitoring position, and further determining the transient response amount of the temperature sensor at each monitoring position; the confidence evaluation is carried out on the critical alarm values at all the monitoring positions, and the confidence alarm values of the fire disaster occur at all the monitoring positions; and for each confidence alarm value, when the confidence alarm value is larger than the confidence alarm threshold value, sending the monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park. By adopting the scheme of the application, the confidence alarm for fire disaster can be realized in the environment where the accuracy of the temperature sensor is interfered in the intelligent park.

Description

Intelligent park fire alarm method, system, equipment and storage medium

Technical Field

The application relates to the technical field of fire alarm, in particular to a fire alarm method, a fire alarm system, fire alarm equipment and a fire alarm storage medium for an intelligent park.

Background

The fire alarm aims at finding fire and giving out an alarm early, and protecting personnel and property safety, utilizes sensors such as a smoke detector, a heat detector and the like to monitor environmental changes, and can automatically trigger an alarm once smoke or abnormal temperature rise is detected, a control panel receives a signal and starts the alarm to inform residents to evacuate and contact emergency services, and the modern system also comprises remote monitoring and smart phone application, so that a user can acquire alarm notification and remote management system state in real time, response speed and system reliability are improved, the efficiency of fire safety management is greatly improved by combining the technologies, casualties and property loss caused by fire are reduced, and the system is an indispensable safety facility for modern buildings and public places.

Integrate intelligent video monitoring and artificial intelligence analysis in current intelligent garden fire alarm, through high definition digtal camera and artificial intelligence (AI, artificial Intelligence) algorithm, can each regional activity of real-time supervision and analysis abnormal conditions to provide automatic fire alarm, however, in intelligent garden there are many factors that interfere with temperature sensor data accuracy, for example: factors such as electromagnetic interference, environmental humidity interference, wind speed interference, voltage interference and mechanical vibration interference can make temperature sensor data become more complicated and uncertain under the influence of the interference factors, so that the authenticity and reliability of the temperature sensor data are difficult to accurately evaluate, the confidence degree of the fire disaster occurrence position of an alarm is low, the influence of electromagnetic interference, environmental humidity, wind speed, voltage and mechanical vibration on fire disaster alarm is comprehensively considered, the problem can be effectively avoided by comprehensively considering the influence of the electromagnetic interference, the environmental humidity, the wind speed, the voltage and the mechanical vibration on the fire disaster alarm, the accuracy of the fire disaster alarm is improved, and therefore, how to realize the confidence alarm on the fire disaster under the environment with the interference on the accuracy of the temperature sensor exists in an intelligent park becomes a difficult problem faced by the industry.

Disclosure of Invention

The application provides a fire alarm method, a fire alarm system, fire alarm equipment and a fire alarm storage medium for an intelligent park, which can realize confidence alarm on fire under the condition that the accuracy of a temperature sensor is interfered in the intelligent park.

In a first aspect, the application provides a fire alarm method for an intelligent park, comprising the following steps:

Acquiring environmental temperature data through temperature sensors arranged at all monitoring positions in a target intelligent park to obtain the environmental temperature data at all the monitoring positions;

Extracting the credible intervals of the environmental temperature data to obtain a plurality of temperature credible domains, determining the temperature diffusion trend of each monitoring position according to all the temperature credible domains, and further determining the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park according to all the temperature diffusion trends;

Acquiring temperature interference data of temperature sensors at all monitoring positions in a target intelligent park to obtain temperature interference records, determining transient fluctuation amounts of the environmental temperatures at the corresponding monitoring positions based on information entropy of the environmental temperature fluctuation at all the monitoring positions and the temperature interference records, and further determining transient response amounts of the temperature sensors at all the monitoring positions in the target intelligent park according to all the transient fluctuation amounts;

Performing confidence evaluation on the critical alarm values at each monitoring position in the target intelligent park according to all the transient response values to obtain confidence alarm values of fire disaster occurring at each monitoring position in the target intelligent park;

and for each confidence alarm value, when the confidence alarm value is larger than the confidence alarm threshold value, sending the monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park.

In some embodiments, extracting the trusted interval from each environmental temperature data to obtain a plurality of temperature trusted domains specifically includes:

Acquiring historical environmental temperature data and historical electromagnetic interference data at each monitoring position;

determining a temperature interference factor of a target intelligent park when the electronic equipment generates electromagnetic interference according to all the historical environmental temperature data and all the historical electromagnetic interference data;

determining a reliable deviation value of the temperature at each monitoring position through the environmental temperature data of each monitoring position;

Determining the upper boundary and the lower boundary of the temperature trusted interval at each monitoring position according to the temperature interference factors and all the trusted deviation values;

And extracting corresponding environmental temperature data through the upper boundary and the lower boundary of the temperature trusted zone of each monitoring position to obtain a plurality of temperature trusted zones.

In some embodiments, determining the temperature diffusion trend for each monitored location based on all of the temperature trusted domains specifically includes:

each temperature trusted domain is standardized, and a plurality of standard temperature trusted domains are obtained;

Performing linear fitting on all standard environment temperature values in each standard temperature trusted domain to obtain a plurality of temperature trusted fitting curves;

And determining the temperature diffusion trend of each monitoring position according to all the temperature credible fitting curves.

In some embodiments, determining the threshold alarm value for the ambient temperature at each monitored location in the target smart park from all the temperature diffusion trends specifically includes:

Acquiring the temperature interference factor;

Determining abnormal temperature early warning values of all monitoring positions in the target intelligent park according to all the temperature diffusion trends;

and determining the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park through the temperature interference factors and all abnormal temperature early warning values.

In some embodiments, determining the amount of transient fluctuation of the ambient temperature at the respective monitoring location based on the entropy of the ambient temperature fluctuation at the respective monitoring location in combination with the temperature disturbance record specifically includes:

Acquiring environmental temperature data at each monitoring position;

Determining information entropy of the environmental temperature fluctuation at each monitoring position according to all the environmental temperature data;

determining an interference fluctuation vector space of the ambient temperature through the temperature interference record;

and determining the transient fluctuation quantity of the ambient temperature at the corresponding monitoring position according to the information entropy of the ambient temperature fluctuation at each monitoring position and the interference fluctuation vector space.

In some embodiments, performing confidence evaluation on the critical alarm values at each monitoring location in the target smart park according to all the transient response amounts, and obtaining the confidence alarm values of the fire disaster at each monitoring location in the target smart park specifically includes:

determining abnormal state fluctuation entropy of the temperature sensor according to all transient response quantities;

converting all transient response volumes into a transient response volume sequence;

Determining abnormal confidence characteristics at each monitoring position in the target intelligent park through the transient response quantity sequence and the abnormal state fluctuation entropy;

And evaluating the confidence alarm values of the fire disaster at each monitoring position of the target intelligent park according to all the abnormal confidence characteristics and all the critical alarm values.

In some embodiments, for each confidence alarm value, when the confidence alarm value is greater than the confidence alarm threshold, sending the monitored location corresponding to the confidence alarm value to the alarm center of the target smart campus specifically includes:

selecting a confidence alarm value;

when the confidence alarm value is smaller than or equal to the confidence alarm threshold value, no processing is carried out;

When the confidence alarm value is larger than the confidence alarm threshold value, sending the monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park;

And repeating the steps, comparing the rest confidence alarm value with a confidence alarm threshold, and sending the monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park when the confidence alarm value is larger than the confidence alarm threshold, so as to finish fire alarm of the target intelligent park.

In a second aspect, the present application provides a fire alarm system for a smart campus, comprising:

The acquisition module is used for acquiring the environmental temperature data through the temperature sensors arranged at all monitoring positions in the target intelligent park to obtain the environmental temperature data at all the monitoring positions;

The processing module is used for extracting the credible intervals of the environmental temperature data to obtain a plurality of temperature credible domains, determining the temperature diffusion trend of each monitoring position according to all the temperature credible domains, and further determining the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park according to all the temperature diffusion trends;

The processing module is further used for collecting temperature interference data of the temperature sensors at all monitoring positions in the target intelligent park to obtain temperature interference records, determining transient fluctuation amounts of the environmental temperatures at the corresponding monitoring positions based on information entropy of the environmental temperature fluctuation at all the monitoring positions and the temperature interference records, and further determining transient response amounts of the temperature sensors at all the monitoring positions in the target intelligent park according to all the transient fluctuation amounts;

the processing module is further used for carrying out confidence evaluation on the critical alarm values at all monitoring positions in the target intelligent park according to all the transient response values to obtain confidence alarm values of fire disasters at all the monitoring positions in the target intelligent park;

and the execution module is used for sending the monitoring position corresponding to the confidence alarm value to the alarm center of the target intelligent park when the confidence alarm value is larger than the confidence alarm threshold value for each confidence alarm value.

In a third aspect, the present application provides a computer device comprising a memory storing code and a processor configured to obtain the code and to perform the smart park fire alarm method described above.

In a fourth aspect, the present application provides a computer readable storage medium storing a computer program which when executed by a processor implements the smart park fire alarm method described above.

The technical scheme provided by the embodiment of the application has the following beneficial effects:

In the intelligent park fire alarm method, system, equipment and storage medium provided by the application, firstly, environmental temperature data are acquired through temperature sensors arranged at all monitoring positions in a target intelligent park, and the environmental temperature data at all monitoring positions are obtained; extracting the credible intervals of the environmental temperature data to obtain a plurality of temperature credible domains, determining the temperature diffusion trend of each monitoring position according to all the temperature credible domains, and further determining the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park according to all the temperature diffusion trends; acquiring temperature interference data of temperature sensors at all monitoring positions in a target intelligent park to obtain temperature interference records, determining transient fluctuation amounts of the environmental temperatures at the corresponding monitoring positions based on information entropy of the environmental temperature fluctuation at all the monitoring positions and the temperature interference records, and further determining transient response amounts of the temperature sensors at all the monitoring positions in the target intelligent park according to all the transient fluctuation amounts; performing confidence evaluation on the critical alarm values at each monitoring position in the target intelligent park according to all the transient response values to obtain confidence alarm values of fire disaster occurring at each monitoring position in the target intelligent park; and for each confidence alarm value, when the confidence alarm value is larger than the confidence alarm threshold value, sending the monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park.

Therefore, the method and the system can carry out confidence evaluation on the critical alarm values at each monitoring position in the target intelligent park according to all transient response values to obtain the confidence alarm values of fire disasters at each monitoring position in the target intelligent park; firstly, performing linear fitting on each temperature trusted domain to obtain temperature diffusion trends for reflecting temperature distribution conditions at each monitoring position, extracting abnormal temperature early warning values for describing deviation degrees of the current temperature at each monitoring position and an expected temperature range from each temperature diffusion trend by combining temperature interference factors, and quantifying all abnormal temperature early warning values to obtain critical warning values for measuring environmental temperature threshold values at the monitoring position, so that abnormal conditions or potential risks of fire disaster at the monitoring position can be recognized; secondly, extracting features of the temperature interference record based on information entropy of environmental temperature fluctuation at each monitoring position to obtain transient fluctuation quantity describing sudden fluctuation degree of the environmental temperature at each monitoring position in a target intelligent park, and quantifying all the transient fluctuation quantities to obtain transient response quantity of the response degree of the temperature sensor at each monitoring position to the transient interference feature, wherein the transient response quantity can reflect the response speed and accuracy of the temperature sensor to fire alarm under transient condition change, thereby helping to improve the applicability and reliability of the temperature sensor in a real-time monitoring and fire alarm system; then, carrying out confidence assessment on critical alarm values at all monitoring positions in the target intelligent park according to all transient response values to obtain confidence alarm values reflecting the reliability degree of the alarms at all monitoring positions, so that the alarm accuracy at all monitoring positions is improved; finally, for each confidence alarm value, when the confidence alarm value is larger than a confidence alarm threshold, sending a monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park; in summary, the scheme of the application can realize confidence alarm for fire disaster in the environment with interference on the accuracy of the temperature sensor in the intelligent park, thereby improving the accuracy of fire disaster alarm.

Drawings

FIG. 1 is an exemplary flow chart of a smart park fire alarm method according to some embodiments of the present application;

FIG. 2 is a schematic flow chart of determining a temperature diffusion trend in some embodiments of the application;

FIG. 3 is a flow chart illustrating the determination of transient fluctuation amounts in some embodiments of the present application;

FIG. 4 is a block diagram of a smart park fire alarm system in accordance with some embodiments of the present application;

fig. 5 is a schematic structural view of a computer device for implementing a fire alarm method for an intelligent campus according to some embodiments of the present application.

Detailed Description

The method is characterized in that environmental temperature data are acquired through temperature sensors arranged at all monitoring positions in a target intelligent park, and the environmental temperature data at all the monitoring positions are obtained; extracting the credible intervals of the environmental temperature data to obtain a plurality of temperature credible domains, determining the temperature diffusion trend of each monitoring position according to all the temperature credible domains, and further determining the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park according to all the temperature diffusion trends; acquiring temperature interference data of temperature sensors at all monitoring positions in a target intelligent park to obtain temperature interference records, determining transient fluctuation amounts of the environmental temperatures at the corresponding monitoring positions based on information entropy of the environmental temperature fluctuation at all the monitoring positions and the temperature interference records, and further determining transient response amounts of the temperature sensors at all the monitoring positions in the target intelligent park according to all the transient fluctuation amounts; performing confidence evaluation on the critical alarm values at each monitoring position in the target intelligent park according to all the transient response values to obtain confidence alarm values of fire disaster occurring at each monitoring position in the target intelligent park; and for each confidence alarm value, when the confidence alarm value is larger than the confidence alarm threshold value, sending the monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park. The scheme of the application can realize confidence alarm for fire disaster in the environment with interference on the accuracy of the temperature sensor in the intelligent park, thereby improving the accuracy of fire disaster alarm.

In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. Referring to fig. 1, which is an exemplary flowchart of a smart campus fire alarm method according to some embodiments of the present application, the smart campus fire alarm method 100 mainly includes the steps of:

in step 101, ambient temperature data at each monitoring location is acquired by temperature sensors disposed at each monitoring location in the target smart park.

In particular, the ambient temperature is acquired at intervals of 30 seconds using temperature sensors arranged at respective monitoring positions in the target smart park, and an ordered sequence of ambient temperature values acquired at intervals of 30 seconds over the past 15 minutes is used as the ambient temperature data at the respective monitoring positions.

In step 102, the trusted interval extraction is performed on each environmental temperature data to obtain a plurality of temperature trusted domains, and the temperature diffusion trend of each monitoring position is determined according to all the temperature trusted domains, so that the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park is determined according to all the temperature diffusion trends.

In some embodiments, the trusted interval extraction is performed on each environmental temperature data, and obtaining a plurality of temperature trusted domains may be implemented by adopting the following steps:

It should be noted that, in the smart industrial area, large-scale electric power equipment and electric power distribution systems are usually included in the present application, such as transformers, electric power generators, electric arc furnaces, etc., these equipment may generate strong electromagnetic fields during operation, especially when a switch is operated or a load is changed, sudden electromagnetic interference may be caused, and the electromagnetic interference may cause a sensor to erroneously identify a non-fire signal as a fire signal, for example, an electromagnetic wave of the equipment operation is mistakenly regarded as smoke or flame, and in addition, the electromagnetic interference may cause a real fire signal to be shielded or partially shielded, so that the fire cannot be accurately detected in time.

In particular, when the historical environmental temperature data and the historical electromagnetic interference data at different monitoring positions are obtained from the database of the data management center of the target intelligent park, it is to be noted that the historical electromagnetic interference data in the application is a sequence composed of electromagnetic interference information acquired every 30 seconds in the past three days, wherein the electromagnetic interference information in the historical electromagnetic interference data comprises electric field intensity and magnetic field intensity, and electromagnetic interference information at the monitoring positions can be measured by using electromagnetic interference measuring equipment (such as a spectrum analyzer).

It should be further noted that, in the present application, the historical ambient temperature data is a sequence of ambient temperature values acquired every 30 seconds in the past three days.

In particular, when determining that the electronic device generates electromagnetic interference according to all the historical environmental temperature data and all the historical electromagnetic interference data, the temperature interference factor of the target intelligent park can be realized by adopting the following modes: firstly, taking historical environment temperature data as dependent variables and historical electromagnetic interference data as independent variables, then training a machine learning model (such as a Support Vector Machine (SVM)) by using all the historical environment temperature data and all the historical electromagnetic interference data, obtaining weight coefficients of the machine learning model about electric field intensity and magnetic field intensity after training, finally carrying out minimum-maximum normalization on the two weight coefficients, adding two values obtained by normalization, and taking the obtained value as a temperature interference factor of a target intelligent park when electromagnetic interference is generated by electronic equipment, wherein other methods can be adopted in other embodiments, and the detailed description is omitted.

It should be noted that, in the present application, the temperature interference factor reflects the influence degree of electromagnetic interference generated by the electronic device on the temperature at each monitoring position in the target smart park, and the greater the temperature interference factor, the greater the influence degree of electromagnetic interference generated by the electronic device on the temperature at each monitoring position in the target smart park, otherwise, the smaller the influence degree of electromagnetic interference generated by the electronic device on the temperature at each monitoring position in the target smart park.

In particular, determining the reliable deviation value of the temperature at each monitoring location according to the environmental temperature data of each monitoring location may be implemented in the following manner, that is: firstly, in order to estimate a higher reliability degree for the environmental temperature data, the confidence level of all the environmental temperature data is set to 99%, wherein the confidence level is a probability value for describing the reliability degree of an estimation result, in other embodiments, different confidence levels can be selected according to actual needs, next, a critical value under the 99% confidence level is obtained through Python, then the total number of environmental temperature values in the environmental temperature data is squared, the value obtained after the square is taken as the total number of the square values, the critical value is divided by the total number of the square values, the obtained value is taken as the confidence critical value, then, the average value is calculated for the environmental temperature data at each monitoring position, each obtained average value is multiplied by the confidence critical value, and each value obtained by the multiplication is taken as the reliable deviation value at the corresponding monitoring position, in other embodiments, other methods can be adopted, and the method is not limited.

It should be noted that, the reliable deviation value in the present application represents the estimation accuracy of the temperature mean value at the monitoring position at the 99% confidence level, and the smaller reliable deviation value means that the estimation of the temperature mean value is more accurate and reliable, so that the reliability of the temperature data at different positions can be estimated, and a basis is provided for further analysis, such as providing a reference when comparing the temperature change trend at different positions.

The determination of the upper and lower boundaries of the temperature trusted intervals at each monitoring position according to the temperature interference factors and all the trusted deviations can be realized in the following ways, namely: selecting a monitoring position, calculating a mean value and a variance of environmental data at the monitoring position, sequentially adding the variance and the credible deviation value at the monitoring position by using the mean value at the position, adding the obtained value to the temperature interference factor, taking the summed value as an upper boundary of a temperature credible interval at the monitoring position, sequentially subtracting the variance and the credible deviation value at the monitoring position by the mean value at the position, subtracting the temperature interference factor, taking the obtained value as a lower boundary of the temperature credible interval at the monitoring position, and repeating the steps to sequentially obtain the upper boundary and the lower boundary of the temperature credible interval at all the remaining monitoring positions, which can be realized by other methods in other embodiments without repeated description.

In specific implementation, the corresponding environmental temperature data is extracted through the upper boundary and the lower boundary of the temperature trusted interval of each monitoring position, and the obtaining of a plurality of temperature trusted domains can be realized in the following manner, namely: selecting one monitoring position, forming an ordered region by environment temperature values of which the environment data is smaller than or equal to the upper boundary of a temperature credible interval at the monitoring position and is larger than or equal to the lower boundary of the temperature credible interval at the monitoring position, taking the obtained region as a temperature credible domain at the monitoring position, and repeating the steps to obtain the temperature credible domains at the rest monitoring positions.

It should be noted that, in the present application, the temperature trusted region is an ordered region composed of an environmental temperature value between an upper boundary of a temperature trusted region and a lower boundary of the temperature trusted region, the temperature trusted region is a trusted region of a temperature parameter at a monitoring position, which means that an actual environmental temperature value is considered to fall in the region with a 99% confidence level under the electromagnetic interference condition, and the temperature trusted region is a region composed of an upper boundary of the temperature trusted region and a lower boundary of the temperature trusted region, wherein the upper boundary of the temperature trusted region is a maximum value under the 99% confidence level according to the total temperature of environmental temperature data, and the lower boundary of the temperature trusted region is a minimum value under the 99% confidence level according to the total temperature of the environmental temperature data, and in addition, it should be noted that one temperature trusted region corresponds to one monitoring position.

In some embodiments, referring to fig. 2, which is a schematic flow chart of determining a temperature diffusion trend in some embodiments of the present application, determining a temperature diffusion trend of each monitoring location according to all temperature trusted domains in this embodiment may be implemented by the following steps:

Firstly, in step 1021, each temperature trusted domain is standardized to obtain a plurality of standard temperature trusted domains;

Next, in step 1022, linear fitting is performed on all the standard environmental temperature values in each standard temperature trusted domain, so as to obtain a plurality of temperature trusted fitting curves;

then, in step 1023, the temperature diffusion trend of each monitored location is determined according to all the temperature credible fitting curves.

In specific implementation, a maximum absolute value standardization algorithm can be adopted to standardize all the environmental temperature values in each temperature trusted domain, the standardized temperature trusted domain is used as a standard temperature trusted domain, and all the values in the standard temperature trusted domain are used as standard environmental temperature values, so that a plurality of standard temperature trusted domains are obtained.

It should be noted that, the maximum absolute value normalization algorithm adopted in the present application does not change the distribution shape of the data in the spatial temperature distribution, and meanwhile, the maximum absolute value normalization is more robust to the processing of abnormal values, and the abnormal values do not have an excessive influence on other data points, and in other embodiments, other methods may be adopted to implement the method, which is not described here again.

In specific implementation, linear fitting is performed on all standard environmental temperature values in each standard temperature trusted domain, and a plurality of temperature trusted fitting curves can be obtained by adopting the following modes: the least square support vector machine algorithm in the prior art is adopted to fit all the standard environmental temperature values in the standard temperature trusted domain, and a curve obtained by fitting is used as a temperature trusted fit curve corresponding to the standard temperature trusted domain, and in other embodiments, the method can be realized by adopting other methods, and the method is not limited herein.

In the application, the values on each temperature credible fitting curve are used as temperature credible fitting values, each temperature credible fitting value corresponds to a standard environment temperature value, and in addition, the application also needs to be explained that one temperature fitting curve corresponds to one monitoring position.

In specific implementation, the temperature diffusion trend of each monitoring position can be determined according to all the temperature credible fitting curves by adopting the following modes: selecting a temperature credible fitting curve, selecting a position with the maximum derivative and a position with the minimum derivative from the temperature credible fitting curve, calculating a slope between the position with the maximum derivative and the position with the minimum derivative, taking the slope as a temperature diffusion trend of the temperature credible fitting curve corresponding to a monitoring position, repeating the steps to obtain a temperature diffusion trend of the residual temperature credible fitting curve corresponding to the monitoring position, and in other embodiments, adopting other methods to realize the temperature diffusion trend, and not being repeated here.

It should be noted that, in the present application, the temperature diffusion trend reflects the diffusion trend of the temperature distribution at the monitored location, and the larger the temperature diffusion trend is, the more obvious the diffusion trend of the temperature distribution at the monitored location is, whereas the less obvious the diffusion trend of the temperature distribution at the monitored location is, the monitoring location with abnormal temperature variation trend can be identified by the temperature diffusion trend, which is helpful for identifying and preventing fire disaster, and in addition, it should be noted that, in the present application, one temperature diffusion trend corresponds to one monitoring location, and each temperature diffusion trend corresponds to one temperature fitting curve.

In some embodiments, determining the threshold alarm value for the ambient temperature at each monitored location in the target smart park from all the temperature diffusion trends may be accomplished by:

Acquiring the temperature interference factor;

In specific implementation, determining abnormal temperature early warning values of each monitoring position in the target intelligent park according to all temperature diffusion trends can be realized by adopting the following modes: selecting a temperature diffusion trend, taking all the temperature credible fitting values on the temperature fitting curve corresponding to the temperature diffusion trend and the standard environmental temperature values corresponding to the temperature credible fitting values as differences, taking the values obtained by the differences as temperature characteristic residual errors corresponding to the standard environmental temperature values, calculating the average value of all the temperature characteristic residual errors, multiplying the obtained average value by the temperature diffusion trend, taking the multiplied value as an abnormal temperature early-warning value of a monitoring position in a target intelligent park corresponding to the temperature diffusion trend, repeating the steps to obtain an abnormal temperature early-warning value of a monitoring position in the target intelligent park corresponding to the residual temperature diffusion trend, and adopting other methods in other embodiments without limitation.

The abnormal temperature early warning value in the application represents the early warning value of the deviation degree of the current temperature and the expected temperature range, the potential temperature change abnormality at the monitoring position of the target intelligent park can be identified through the abnormal temperature early warning value, and corresponding preventive or countermeasure measures are taken.

In specific implementation, the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park can be determined by the temperature interference factor and all abnormal temperature early-warning values by adopting the following modes: firstly, selecting an abnormal temperature early-warning value with the largest value and an abnormal temperature early-warning value with the smallest value from all abnormal temperature early-warning values, subtracting the smallest abnormal temperature early-warning value from the largest abnormal temperature early-warning value, multiplying the subtracted value by the temperature interference factor, multiplying the multiplied value as a fire alarm interval value, multiplying each abnormal temperature early-warning value by the fire alarm interval value, and taking all the multiplied values as critical alarm values of the environmental temperature at the monitoring position in the intelligent park corresponding to each abnormal temperature early-warning value.

It should be noted that, in the present application, the critical alarm value represents a threshold value of the environmental temperature at the monitoring location, and when the environmental temperature reaches or exceeds the threshold value, an alarm is triggered, and an abnormal situation or a potential risk of a fire at each monitoring location in the smart park can be monitored by using the critical alarm value, so as to take necessary countermeasures.

In step 103, temperature interference data of temperature sensors at each monitoring position in the target intelligent park are collected to obtain temperature interference records, transient fluctuation amounts of the environmental temperatures at the corresponding monitoring positions are determined based on information entropy of the environmental temperature fluctuation at each monitoring position and the temperature interference records, and then transient response amounts of the temperature sensors at each monitoring position in the target intelligent park are determined according to all the transient fluctuation amounts.

And in the specific implementation, acquiring temperature interference data of temperature sensors at each monitoring position in the interference target intelligent park, and taking a set formed by all the temperature sensing data as a humidity interference record.

It should be noted that, the humidity at the monitoring position in the present application may affect the performance of the temperature sensor (such as a resistance temperature detector), resulting in inaccurate readings; the stability of the supply voltage to which the temperature sensor is connected at the monitoring location can affect the operating state of the electronic components of the temperature sensor, leading to measurement errors or temporary failure of the sensor; wind speed at the monitoring location can affect air flow around the temperature sensor, resulting in a deviation of the temperature measured by the temperature sensor from the actual ambient temperature, e.g., strong winds may carry away heat, making the temperature sensor readings lower; vibration and shock generated by mechanical equipment in the vicinity of the monitoring location during operation can affect the mounting stability of the temperature sensor, resulting in momentary fluctuations or errors in readings.

It should be noted that, in the present application, the temperature interference data is composed of humidity data, wind speed data, voltage data and mechanical vibration data, and the humidity data, wind speed data, voltage data and mechanical vibration data are all used as temperature interference component data, and all values in the temperature interference component data are used as temperature interference component values, wherein, humidity is collected once at 30 seconds intervals by using a humidity sensor at a monitoring position, and an ordered sequence composed of humidity values collected at 30 seconds intervals in the past 15 minutes is used as humidity data at the monitoring position; collecting primary wind speed at a monitoring position by using a hot wire anemometer at intervals of 30 seconds, and taking an ordered sequence of wind speed values obtained by collecting the primary wind speed at intervals of 30 seconds in the past 15 minutes as wind speed data at the monitoring position; collecting voltage at the monitoring position by using a voltage sensor at intervals of 30 seconds, and taking an ordered sequence of voltage values obtained by collecting the voltage values at intervals of 30 seconds in the past 15 minutes as voltage data at the monitoring position; vibration amplitude was acquired at the monitoring position using a vibration sensor at 30-second intervals, and an ordered sequence of vibration amplitude values acquired at 30-second intervals over the past 15 minutes was used as mechanical vibration data at the monitoring position.

In some embodiments, referring to fig. 3, which is a schematic flow chart of determining transient fluctuation amounts in some embodiments of the present application, determining transient fluctuation amounts of ambient temperature at corresponding monitoring positions based on information entropy of ambient temperature fluctuation at each monitoring position in combination with the temperature interference record in this embodiment may be implemented by the following steps:

first, in step 1031, ambient temperature data at each monitoring location is acquired;

next, in step 1032, determining entropy of the environmental temperature fluctuations at each monitored location based on all of the environmental temperature data;

Then, in step 1033, determining an interference fluctuation vector space of the ambient temperature from the temperature interference record;

finally, in step 1034, the transient fluctuation amount of the environmental temperature at the corresponding monitoring position is determined according to the information entropy of the environmental temperature fluctuation at each monitoring position and the interference fluctuation vector space.

It should be noted that, the information entropy of the environmental temperature fluctuation in the present application is an index for measuring the uncertainty and complexity in the environmental temperature data, which quantifies the randomness and distribution characteristics of the environmental temperature data, and the higher the information entropy value is indicative of the abnormal situation in the intelligent industrial park, for example, the sudden temperature change indicates the equipment failure or the local high temperature event, as a preferred embodiment, the information entropy of the environmental temperature fluctuation at each monitoring position is determined according to all the environmental temperature data, which is implemented in the following manner, that is: for each monitoring position, calculating probability distribution of temperature values in environmental data at each monitoring position by using kernel density estimation (such as Gaussian kernel density estimation), carrying out entropy feature description on the probability distribution obtained at each monitoring position by using information entropy in the prior art, and taking the value obtained after the entropy feature description as the information entropy of environmental temperature fluctuation at the corresponding monitoring position.

In specific implementation, the interference fluctuation vector space for determining the ambient temperature through the temperature interference record can be realized by adopting the following modes: firstly, creating a sliding window, taking the total number of temperature interference components in the temperature interference data recorded by the temperature interference as a window step length, setting the length of the sliding window as a numerical value with the same size as the window step length, then selecting one temperature interference data from the temperature interference records, selecting one temperature interference component data from the temperature interference data, aligning the sliding window with the first temperature interference component value in the temperature interference component data, selecting the maximum temperature interference component value from the temperature interference component values covered by the sliding window, and taking the maximum temperature interference component value as the maximum temperature interference component value, moving the sliding window, aligning the sliding window with the second temperature interference component value in the temperature interference component data, selecting the maximum temperature interference component value from the temperature interference component values covered by the sliding window, and repeating the above steps with the maximum temperature interference component value as the maximum temperature interference component value, sequentially moving the sliding window until the first position of the sliding window aligns with the last temperature interference component value in the temperature interference component data, and all the obtained maximum temperature interference component values are formed into vectors according to the moving sequence of the sliding window, the vectors are used as interference fluctuation vectors of the ambient temperature, the steps are repeated, the same processing is carried out on the temperature interference component data of all the temperature interference data in the temperature interference record to obtain the residual interference fluctuation vector, and the set of all interference fluctuation vectors is used as the interference fluctuation vector space, and in other embodiments, the interference fluctuation vector space can be realized by other methods, which is not limited herein.

In the application, the interference fluctuation vector space is a set formed by a plurality of interference fluctuation vectors, wherein the interference fluctuation vectors in the interference fluctuation vector space represent abnormal fluctuation sequences generated by different types of data of the ambient temperature at the interference monitoring position, and the abnormal condition of the sensor equipment can be rapidly monitored through the interference fluctuation vectors, so that the trend characteristics of interference are extracted from the interference fluctuation vectors.

In specific implementation, determining the transient fluctuation amount of the environmental temperature at the corresponding monitoring position according to the information entropy of the environmental temperature fluctuation at each monitoring position and the interference fluctuation vector space can be realized in the following manner that: firstly, a clustering algorithm (such as k-means clustering, which is not limited herein) is used to extract outliers from each interference fluctuation vector in an interference fluctuation vector space, wherein the outliers represent maximum temperature interference component values, then, the mean value of the outliers of each interference fluctuation vector is calculated, each obtained mean value is used as the outlier mean value of the corresponding interference fluctuation vector, a monitoring position is selected, the information entropy of the environmental temperature fluctuation at the monitoring position is multiplied with the outlier mean value of each interference fluctuation vector, each value obtained after multiplication is added, the value obtained after addition is used as the transient fluctuation amount of the environmental temperature at the monitoring position, the steps are repeated to obtain the transient fluctuation amount of the environmental temperature at the rest monitoring position, and other methods can be adopted in other embodiments to realize the method, which are not repeated herein.

The transient fluctuation amount in the application represents the degree of sudden fluctuation when the ambient temperature at the monitoring position is interfered, and the larger the transient fluctuation amount is, the higher the degree of sudden fluctuation when the ambient temperature at the monitoring position is interfered is, and vice versa, and each transient fluctuation amount in the application corresponds to one monitoring position.

In some embodiments, determining the transient response of the temperature sensor at each monitoring location in the target smart park from all the transient fluctuations may be accomplished by:

Acquiring the temperature interference factor;

Determining a transient disturbance characteristic sequence of the temperature sensor according to the temperature disturbance factors and all transient fluctuation amounts;

and determining the transient response of the temperature sensor at each monitoring position in the target intelligent park through the transient interference characteristic sequence.

In specific implementation, the transient interference characteristic sequence of the temperature sensor according to the temperature interference factor and all transient fluctuation amounts can be realized in the following manner that: and sorting all transient fluctuation amounts in ascending order, taking the sorted sequence as a transient fluctuation sequence, selecting a first transient fluctuation amount from the transient fluctuation sequence, subtracting the first transient fluctuation amount from a second transient fluctuation amount, dividing the subtracted value by the temperature interference factor, taking the subtracted value as a transient interference characteristic value of the first transient fluctuation amount, selecting a second transient fluctuation amount from the transient fluctuation sequence, subtracting the second transient fluctuation amount from a third transient fluctuation amount, dividing the subtracted value by the temperature interference factor, taking the subtracted value as a transient interference characteristic value of the second transient fluctuation amount, repeating the steps until the first transient fluctuation amount is counted down in the transient fluctuation sequence, subtracting the mean value of the transient fluctuation sequence from the first transient fluctuation amount, dividing the subtracted value by the temperature interference factor, taking the subtracted value as a transient interference characteristic value of the first transient fluctuation amount, and in other embodiments, realizing other methods without limitation.

It should be noted that, in the present application, the transient interference feature sequence is an ordered sequence composed of a plurality of transient interference feature values, where the transient interference feature values in the transient interference feature sequence are used to identify or identify abnormal behaviors of the temperature sensor when the temperature sensor is subjected to sudden interference, so as to help to quickly locate the monitoring position.

In specific implementation, the transient response of the temperature sensor at each monitoring position in the target intelligent park can be determined by the transient interference feature sequence by adopting the following modes: selecting a first transient interference characteristic value from the transient interference characteristic sequence, calculating a linear distance between a monitoring position corresponding to the first transient interference characteristic value and a monitoring position corresponding to the second transient interference characteristic value, dividing the first transient interference characteristic value by the linear distance between the monitoring position corresponding to the first transient interference characteristic value, taking the divided value as a transient response of a temperature sensor at the monitoring position corresponding to the first transient interference characteristic value, selecting a second transient interference characteristic value from the transient interference characteristic sequence, calculating the linear distance between the monitoring position corresponding to the second transient interference characteristic value and the monitoring position corresponding to the third transient interference characteristic value, dividing the second transient interference characteristic value by the linear distance between the monitoring position corresponding to the second transient interference characteristic value, taking the divided value as a transient response of a temperature sensor at the monitoring position corresponding to the second transient interference characteristic value, repeating the steps until the first transient interference characteristic value is counted, calculating the linear distance between the monitoring position corresponding to the first transient interference characteristic value and the monitoring position corresponding to the first transient interference characteristic value, calculating the linear distance between the first transient interference characteristic value and the monitoring position corresponding to the first transient interference characteristic value, and taking the first transient interference characteristic value and the second transient interference characteristic value as the linear distance between the first transient interference characteristic value and the second transient interference characteristic value, and taking the calculated value between the first transient interference characteristic value and the reciprocal value and the second transient response of the temperature sensor at the monitoring position to be calculated.

The transient response quantity refers to quantitative description of the response degree of the temperature sensor at the monitoring position to the transient interference characteristic, and the transient response quantity can reflect the response speed and accuracy of the temperature sensor to fire alarm under transient condition change, so that the applicability and reliability of the temperature sensor in a real-time monitoring and fire alarm system are improved.

In step 104, confidence evaluation is performed on the critical alarm values at each monitoring position in the target intelligent park according to all the transient response values, so as to obtain the confidence alarm values of fire disaster at each monitoring position in the target intelligent park.

In some embodiments, the confidence evaluation is performed on the critical alarm values at each monitoring location in the target smart park according to all the transient response amounts, so as to obtain the confidence alarm values of the fire disaster at each monitoring location in the target smart park, which is implemented by the following steps:

It should be noted that, in the present application, the abnormal state fluctuation entropy of the temperature sensor refers to a measure of the intensity of abnormal fluctuation in the output data of the temperature sensor, where the greater the intensity of abnormal fluctuation in the output data of the temperature sensor, and vice versa, as a preferred embodiment, the determining the abnormal state fluctuation entropy of the temperature sensor according to all transient response amounts may be implemented in the following manner, that is: and carrying out entropy feature description on all transient response values through information entropy in the prior art, and taking a value obtained after the entropy feature description as abnormal state fluctuation entropy of the temperature sensor.

In specific implementation, the conversion of all transient response values into a transient response value sequence can be realized by the following modes: and (3) carrying out ascending order sequencing on all the transient response values, and taking the sequence obtained after sequencing as a transient response value sequence.

In specific implementation, determining the abnormal confidence characteristics at each monitoring position in the target intelligent park through the transient response volume sequence and the abnormal state fluctuation entropy can be realized by adopting the following modes: firstly, dividing all transient response values in the transient response value sequence by abnormal state fluctuation entropy, taking the transient response value sequence divided by the abnormal state fluctuation entropy as a state response sequence, taking each value in the state response sequence as a state response value, wherein each state response value corresponds to a monitoring position, then selecting a first state response value from the state response sequence, then selecting a state response value which is closest to the first state response value and has the same number as a window step value from the state response sequence, thereby forming an ordered sequence, taking the ordered sequence as an abnormal confidence characteristic of a monitoring position in a target smart zone corresponding to the first state response value, then selecting a second state response value from the state response sequence, then selecting a state response value which is closest to the second state response value and has the same number as the window step value from the state response sequence, further taking the ordered sequence as a state response value which corresponds to the second state response value and has the same number as the window step value, repeatedly carrying out the monitoring position in the target smart zone corresponding to the abnormal confidence characteristic in the second state response value, and carrying out the monitoring position in the other smart zone, and carrying out other abnormal state characteristics in turn.

The abnormal confidence features reflect the sequence of the credibility degree when abnormal states are generated among the monitoring positions within a certain range, the effective range of the abnormal states generated at different monitoring positions can be judged through the abnormal confidence features, the potential abnormal states in the environmental temperature data can be rapidly identified, and in addition, each abnormal confidence feature corresponds to one monitoring position.

The confidence alarm value in the application reflects the parameter value of the credibility degree when the monitoring position is alarmed, and the larger the confidence alarm value is, the larger the credibility degree when the monitoring position of the target intelligent park is alarmed is, namely: the higher the accuracy of alarming the monitoring positions of the target intelligent park, as a preferred embodiment, the confidence alarm values of fire disaster occurring at each monitoring position of the target intelligent park can be estimated according to all abnormal confidence characteristics and all critical alarm values by the following modes, namely: selecting an abnormal confidence feature, calculating variances for all the rest abnormal confidence features, taking the abnormal confidence feature with the largest variance value as a target abnormal confidence feature, multiplying all state response values in the abnormal confidence feature by a critical alarm value at a monitoring position corresponding to the abnormal confidence feature, summing all multiplied values, dividing the summed value by the largest state response value in the target abnormal confidence feature, taking the divided value as a confidence alarm value at the monitoring position corresponding to the abnormal confidence feature in the target intelligent park, and repeating the steps to obtain the confidence alarm value at the monitoring position corresponding to the rest abnormal confidence feature in the target intelligent park.

In step 105, for each confidence alarm value, when the confidence alarm value is greater than the confidence alarm threshold, the monitored location corresponding to the confidence alarm value is sent to the alarm center of the target smart park.

In some embodiments, for each confidence alarm value, when the confidence alarm value is greater than the confidence alarm threshold, sending the monitored location corresponding to the confidence alarm value to the alarm center of the target smart campus may be implemented by:

selecting a confidence alarm value;

And repeating the steps, comparing the rest confidence alarm value with a confidence alarm threshold, and sending the monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park when the confidence alarm value is larger than the confidence alarm threshold, so as to finally realize fire alarm of the target intelligent park.

When the confidence alarm value is larger than the confidence alarm threshold, the sensor at the monitoring position corresponding to the confidence alarm value is used for transmitting the position coordinate of the sensor to the alarm center of the target intelligent park.

It should be noted that, the confidence alarm threshold value in the present application may be determined by using a percentile method, for example: firstly, the 90 th percentile is taken as a confidence alarm threshold value, all confidence alarm values are ordered to obtain a sequence, for example, x= [2,3,4,5,6,9, 11, 13, 20, 25, 30], secondly, the 90 th percentile position is calculated to be 0.9× (10+1) =9.9, then the weighted average of the 9 th and 10 th confidence alarm values is taken, namely, the confidence alarm threshold value=25+0.9× (30-25) =29.5, and the confidence alarm threshold value can be determined according to actual needs in other embodiments without limitation.

Additionally, in another aspect of the present application, in some embodiments, the present application provides a smart campus fire alarm system, referring to fig. 4, which is a schematic diagram of exemplary hardware and/or software of a smart campus fire alarm system according to some embodiments of the present application, the smart campus fire alarm system 400 comprising: the acquisition module 401, the processing module 402, and the execution module 403 are respectively described as follows:

The acquisition module 401 is mainly used for acquiring environmental temperature data through temperature sensors arranged at all monitoring positions in a target intelligent park to obtain the environmental temperature data at all the monitoring positions;

The processing module 402 is configured to perform trusted interval extraction on each environmental temperature data to obtain a plurality of temperature trusted domains, determine a temperature diffusion trend of each monitoring position according to all the temperature trusted domains, and further determine a critical alarm value of the environmental temperature at each monitoring position in the target smart park according to all the temperature diffusion trends;

It should be noted that, in the present application, the processing module 402 is further configured to collect temperature interference data of temperature sensors at each monitoring location in the target smart park, obtain a temperature interference record, determine transient fluctuation amounts of ambient temperature at corresponding monitoring locations based on information entropy of ambient temperature fluctuation at each monitoring location and the temperature interference record, and further determine transient response amounts of the temperature sensors at each monitoring location in the target smart park according to all the transient fluctuation amounts;

In addition, the processing module 402 is further configured to perform confidence evaluation on the critical alarm values at each monitoring location in the target smart park according to all the transient response amounts, so as to obtain confidence alarm values of fire disaster occurring at each monitoring location in the target smart park;

the execution module 403 is configured to, for each confidence alarm value, send a monitoring location corresponding to the confidence alarm value to an alarm center of the target smart park when the confidence alarm value is greater than a confidence alarm threshold.

In addition, the application also provides a computer device, which comprises a memory and a processor, wherein the memory stores codes, and the processor is configured to acquire the codes and execute the intelligent park fire alarm method.

In some embodiments, reference is made to FIG. 5, which is a schematic diagram illustrating a computer device employing a smart campus fire alarm method, according to some embodiments of the application. The smart park fire alarm method of the above embodiments may be implemented by a computer device shown in fig. 5, where the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

The processor 501 may be a general purpose central processing unit (central processing unit, CPU), an application-specific integrated circuit (ASIC), or one or more of the methods for controlling the performance of the intelligent campus fire alarm method of the present application.

Communication bus 502 may be used to transfer information between the above-described components.

The Memory 503 may be, but is not limited to, a read-only Memory (ROM) or other type of static storage device that can store static information and instructions, a random access Memory (random access Memory, RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-only Memory, EEPROM), a compact disc (compact disc read-only Memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 503 may be separate and coupled to the processor 501 via a communication bus 502. Memory 503 may also be integrated with processor 501.

Wherein the memory 503 is for storing program codes for executing the inventive arrangements and is controlled for execution by the processor 501. The processor 501 is configured to execute program code stored in the memory 503. One or more software modules may be included in the program code. The methods described in the above method embodiments may be implemented by one or more software modules in program code in the processor 501 and in the memory 503.

Communication interface 504, using any transceiver-like device, is used to communicate with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), etc.

In a specific implementation, as an embodiment, a computer device may include a plurality of processors, where each of the processors may be a single-core (single-CPU) processor or may be a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).

The computer device may be a general purpose computer device or a special purpose computer device. In a specific implementation, the computer device may be a desktop, a laptop, a web server, a personal computer (PDA), a mobile handset, a tablet, a wireless terminal device, a communication device, or an embedded device. Embodiments of the application are not limited to the type of computer device.

In addition, the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the intelligent park fire alarm method when being executed by a processor.

In summary, in the intelligent park fire alarm method, system, equipment and storage medium disclosed by the embodiment of the application, environmental temperature data are acquired through temperature sensors arranged at all monitoring positions in a target intelligent park, so that the environmental temperature data at all monitoring positions are obtained; extracting the credible intervals of the environmental temperature data to obtain a plurality of temperature credible domains, determining the temperature diffusion trend of each monitoring position according to all the temperature credible domains, and further determining the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park according to all the temperature diffusion trends; acquiring temperature interference data of temperature sensors at all monitoring positions in a target intelligent park to obtain temperature interference records, determining transient fluctuation amounts of the environmental temperatures at the corresponding monitoring positions based on information entropy of the environmental temperature fluctuation at all the monitoring positions and the temperature interference records, and further determining transient response amounts of the temperature sensors at all the monitoring positions in the target intelligent park according to all the transient fluctuation amounts; performing confidence evaluation on the critical alarm values at each monitoring position in the target intelligent park according to all the transient response values to obtain confidence alarm values of fire disaster occurring at each monitoring position in the target intelligent park; and for each confidence alarm value, when the confidence alarm value is larger than the confidence alarm threshold value, sending the monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park. The scheme of the application can realize confidence alarm for fire disaster in the environment with interference on the accuracy of the temperature sensor in the intelligent park, thereby improving the accuracy of fire disaster alarm.

While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. The intelligent park fire alarm method is characterized by comprising the following steps:

Extracting the credible intervals of the environmental temperature data to obtain a plurality of temperature credible domains, determining the temperature diffusion trend of each monitoring position according to all the temperature credible domains, wherein the temperature diffusion trend reflects the diffusion trend of the temperature distribution at the monitoring position, and further determining the critical alarm value of the environmental temperature at each monitoring position in the target intelligent park according to all the temperature diffusion trends;

Acquiring temperature interference data of temperature sensors at all monitoring positions in a target intelligent park to obtain temperature interference records, determining transient fluctuation amounts of the environmental temperatures at the corresponding monitoring positions based on the information entropy of the environmental temperature fluctuation at all monitoring positions and the temperature interference records, wherein the information entropy of the environmental temperature fluctuation is an index for measuring uncertainty and complexity in the environmental temperature data, and further determining transient response amounts of the temperature sensors at all monitoring positions in the target intelligent park according to all the transient fluctuation amounts;

Carrying out confidence evaluation on critical alarm values at all monitoring positions in the target intelligent park according to all transient response values to obtain confidence alarm values of fire disasters at all monitoring positions in the target intelligent park, wherein the confidence alarm values reflect parameter values of the credibility degree when alarming is carried out at the monitoring positions;

For each confidence alarm value, when the confidence alarm value is larger than a confidence alarm threshold, sending a monitoring position corresponding to the confidence alarm value to an alarm center of the target intelligent park;

the method for extracting the trusted interval of each environmental temperature data to obtain a plurality of temperature trusted domains specifically comprises the following steps:

extracting corresponding environmental temperature data through the upper boundary and the lower boundary of the temperature trusted zone of each monitoring position to obtain a plurality of temperature trusted zones;

the determining the temperature diffusion trend of each monitoring position according to all the temperature trusted domains specifically comprises the following steps:

determining the temperature diffusion trend of each monitoring position according to all the temperature credible fitting curves;

The determining the transient fluctuation amount of the environmental temperature at the corresponding monitoring position based on the information entropy of the environmental temperature fluctuation at each monitoring position and the temperature interference record specifically comprises the following steps:

Acquiring environmental temperature data at each monitoring position;

determining transient fluctuation quantity of the environmental temperature at the corresponding monitoring position according to the information entropy of the environmental temperature fluctuation at each monitoring position and the interference fluctuation vector space;

the method for obtaining the confidence alarm value of the fire disaster at each monitoring position of the target intelligent park comprises the following steps of:

2. The method of claim 1, wherein determining the threshold warning value for the ambient temperature at each monitored location in the target smart park from all the temperature diffusion trends comprises:

Acquiring the temperature interference factor;

3. The method of claim 1, wherein for each confidence alarm value, when the confidence alarm value is greater than the confidence alarm threshold, transmitting the monitored location corresponding to the confidence alarm value to the alarm center of the target smart campus comprises:

selecting a confidence alarm value;

4. A smart campus fire alarm system for fire alarm using the method of any one of claims 1 to 3, the smart campus fire alarm system comprising:

5. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the smart park fire alarm method of any one of claims 1 to 3 when the computer program is executed.

6. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the smart park fire alarm method of any one of claims 1 to 3.