A fire protection Internet of Things alarm data collection and redundant data processing device based on NB-iot

Abstract

The present invention provides a device for collecting and processing redundant data based on NB‑iot fire Internet of Things alarm data, and relates to the technical field of redundant data processing of Internet of Things. The present invention includes the following modules: a data collection module: collecting environmental data with timestamps and standard data with timestamps of a target area. A data transmission module: transmitting the above data using NB‑iot technology. A preliminary processing module: combining the timestamp with the corresponding data to generate a hash value, and processing repetitive redundant data through hash value identification. A data analysis module: generating a fire generation index through the environmental data and standard data of each sub-area, performing threshold processing on the fire generation index to obtain a fire index, and generating a fire assessment index according to the fire index of each sub-area. A comprehensive processing module: performing threshold analysis on the fire assessment index to determine the occurrence of a fire, and identifying and deleting invalid redundant data according to the occurrence of a fire.

Description

Technical Field

[0001] The present invention relates to the technical field of redundant data processing of Internet of Things, and in particular to a device for collecting and processing redundant data of firefighting Internet of Things alarm data based on NB-iot. Background Art

[0002] As a low-power wide area network technology designed for the Internet of Things, NB-IoT has shown significant advantages in firefighting Internet of Things applications. Its low power consumption allows the equipment to operate stably for a long time without frequent battery replacement, which is essential to ensure the timely response of firefighting equipment in emergency situations. NB-IoT can quickly transmit data collected by various sensors in real time; at the same time, NB-IoT provides a wider coverage range than traditional mobile networks, and data collected even in remote areas can be transmitted smoothly. NB-IoT has strong anti-interference capabilities. When a fire occurs, even if the signal is poor, NB-IoT can smoothly transmit the collected data to the required module.

[0003] In NB-IoT technology, efficient data collection, transmission and processing not only ensures a quick and accurate response to fires, but also significantly improves overall safety management efficiency. However, NB-IoT technology faces great challenges, especially in data management and real-time performance. The problem of data redundancy is particularly prominent in NB-IoT technology, with frequent repeated data collection and processing, which not only consumes a lot of computing resources but also reduces the response speed of the system. It may also cause errors in key information in emergency situations, further affecting the efficiency and accuracy of disaster response. A set of redundant data processing methods is essential.

[0004] In the prior art, the publication number CN110389881A discloses a method for processing redundant data of an SD card based on an alarm function. It includes real-time judgment on whether there is an alarm message. When there is an alarm message, the driving cycle before the alarm message M driving cycle data is deleted, and the latest M driving cycle data is retained. If the driving cycle is less than M, all data is retained, and the retained data is backed up after failure and uploaded to the cloud platform; when the stored driving cycle exceeds M, the driving cycle before M driving cycles is deleted. The prior art can meet the function of saving real-time data during the research and development process, and can delete redundant data to relieve the storage pressure of the SD card, and can ensure that valid data is obtained for analysis when an alarm is issued. The prior art still has shortcomings. First of all, effective management and processing of data redundancy is a core component for improving the efficiency of the data processing system. It is particularly critical to identify and eliminate the duplication of the same data to ensure the accuracy and rapidity of the system response. The prior art is insufficient in processing the redundancy of the same data, and ignores this crucial processing step. Secondly, when processing redundant data, current technology relies heavily on the generation mechanism of alarm information. This decision-making process is crucial for the identification of redundant data. However, the existing technology does not provide clear instructions on its generation and processing, which leads to doubts about the reliability of the existing technology. In addition, when a failure occurs, data transmission may encounter problems. This processing method of the existing technology is based on the normal data transmission system. If there is a problem with the data transmission system, the authenticity and integrity of the data cannot be guaranteed.

[0005] 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 constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a fire protection Internet of Things alarm data collection and redundant data processing device based on NB-iot to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A device for collecting and processing redundant data based on NB-iot firefighting Internet of Things alarm data, the specific structure of which includes:

[0009] Data acquisition module, used to divide the target area into equal areas sub-areas, collecting standard data and environmental data with time stamps for each area through fire alarm controllers and electrical fire controllers; the environmental data include temperature, smoke density, circuit current and circuit voltage; the standard data include standard temperature, standard maximum circuit current, standard minimum circuit current, standard maximum circuit voltage and standard minimum circuit voltage;

[0010] A data transmission module is used to set up an NB-IOT module in the circuit board, and use NB-IOT technology to transmit the standard data and environmental data with timestamps to the fire safety integrated safety management platform for analysis and processing by sending the operator's Internet of Things platform path; the NB-IOT module includes two modes: normal and offline;

[0011] The initial redundancy processing module is used to copy the environmental data and the standard data into two copies, perform data preprocessing on each category of environmental data, and then perform hash value calculations on the preprocessed data respectively, and form corresponding hash value data sets with the calculation results of each category of data respectively, and map the data values ​​of each category of data in the original data set with the hash values ​​in the hash value data set of the same category of data one by one, and divide each category of hash value data set into corresponding independent hash value data sets and repeated hash value data sets, and map them back to the original data set to form corresponding independent data sets and repeated data sets, and define the data in the corresponding repeated data sets as repeated redundant data, and the fire safety integrated safety management platform will actively shield the repeated redundant data;

[0012] A data analysis module, for generating a fire generation index by mathematically analyzing independent data sets of environmental data and standard data of each sub-area, performing threshold processing on the fire generation index to obtain a fire index, and generating a fire assessment index according to the fire index of each sub-area;

[0013] The comprehensive processing module is used to compare the fire assessment index with a preset threshold value, determine the occurrence of the fire according to the comparison result, determine whether the data is invalid redundant data according to the fire occurrence, store non-invalid redundant data in the TF storage card, and automatically delete it after 1 month; directly delete the invalid redundant data.

[0014] Furthermore, if the NB-IOT module is in normal mode, the data will be directly transmitted to the integrated fire and safety management platform for processing; if the NB-IOT module is in offline mode; when the circuit board is abnormally connected to the operator's IoT platform, the alarm data is cached in the TF card as an offline mark. When connected to the operator's IoT platform, a heartbeat packet is used to prompt that there is offline data that needs to be actively requested to be received. When the fire and safety integrated management platform and the operator's IoT platform are offline at the northbound interface, and the alarm data is sent without receiving confirmation from the integrated security platform, the information is automatically written to the TF storage card, and then the platform interacts with the heartbeat packet to remind the platform that there is offline data that has not been received. The integrated fire and safety management platform actively sends a command to obtain the offline package information.

[0015] Furthermore, the preprocessing operation is to concatenate the data with the timestamp to form concatenated data, and then convert the concatenated data into binary for representation. The specific logic for obtaining the concatenated data is: converting the timestamp into a Unix timestamp, and concatenating the Unix timestamp with the collected data to obtain the concatenated data; the specific formula for converting the timestamp into a Unix timestamp is:

[0016]

[0017] in, is a Unix timestamp, is the year of the timestamp, is the month of the timestamp, The day of the timestamp, When the timestamp is is the time stamp, The seconds of the timestamp;

[0018] The specific formula for concatenating Unix timestamps and collected data is:

[0019]

[0020] To splice data, is the Unix timestamp, Indicates a connection operation. The environmental data and standard data.

[0021] Furthermore, a 1 is added to the end of the concatenated data, and then 0 is added until the data length reaches 448 bits, and finally a 64-bit block is added to the end of the data; a set of initial hash values ​​is selected, and the 16 32-bit words represented by the 512-bit block are expanded to 64 32-bit words, and the hash value is compressed using 8 working variables, processed block by block, and the hash value processed by the previous block is used as the initial value to perform the above compression on the next block, and the hash value is updated until the 64 32-bit words are processed to obtain the final updated hash value, and the final updated hash values ​​are connected to obtain the output hash value.

[0022] The initial hash value is the first 32 bits of the binary representation of the decimal part of the square roots of the first 8 prime numbers, and the expansion and compression are both results of data logic processing.

[0023] Furthermore, the specific logic for generating the fire generation index is: the fire generation index is generated by mathematically analyzing the environmental data and standard data in the independent data sets of each sub-area, and the specific formula is:

[0024]

[0025]

[0026]

[0027] in, For the The fire generation index of each sub-region, is the current deviation, is the voltage deviation, For the The smoke concentration in each sub-area, For the The circuit current in each sub-region, For the The circuit voltage of each sub-region, For the The temperature of the sub-region, is the standard maximum circuit current, is the standard minimum circuit current, is the standard maximum circuit voltage, is the standard minimum circuit voltage, is the standard temperature.

[0028] Furthermore, the specific logic of generating the fire index is: compare the fire generation index with the preset fire threshold, set the fire generation index less than or equal to the fire threshold to zero, and subtract the fire threshold from the fire generation index greater than the fire threshold; the specific formula is:

[0029]

[0030] in, For the The fire index of each sub-region, For the The fire generation index of each sub-region, is the fire threshold.

[0031] Furthermore, the specific logic for generating the fire assessment index is: the fire assessment index is obtained by summing the fire indexes of each sub-area, and the specific formula is:

[0032]

[0033] in, is the fire assessment index, is the number of areas with a fire index greater than 0, is the total number of regions, is the fire coverage rate, For the The fire index of each sub-area.

[0034] Furthermore, the specific logic for defining invalid redundant data is as follows: If the fire assessment index , a fire occurs in the target area, the environmental data and standard data at this time are marked as non-redundant data, and the non-invalid redundant data are stored in the TF storage card and automatically deleted after 1 month; if the fire assessment index , then no fire occurs in the target area, the environmental data and standard data at this time are defined as invalid redundant data, and the invalid redundant data are directly deleted;

[0035] in, is the fire assessment index, Assess threshold values ​​for fire.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows: in the process of generating hash values, the present invention adopts an innovative method, that is, closely combining the timestamp with the data content to generate the hash value. This method effectively avoids the common hash value conflict problem in the traditional hash algorithm. By incorporating the timestamp as a key element into the hash generation process, the present invention not only enhances the uniqueness of the hash value, but also improves the verification efficiency of the data integrity. This hash generation strategy combined with the timestamp ensures that even when the data content is similar or identical, the generated hash value will be completely different due to the difference in the timestamp, thereby completely eliminating the hidden danger of hash conflict.

[0037] The present invention creatively constructs a fire assessment index, which is intended to determine whether a fire requires additional firefighting resources. Modern buildings usually have a fire response system with a certain degree of effectiveness, which is sufficient to handle minor fires. There is no need to consume additional precious firefighting resources to handle such fires. The storage value of such data is not great. The present invention effectively distinguishes data with important analytical value through the fire assessment index, and identifies those data with low value or even worthless data. These data identified as invalid and redundant will then be deleted, thereby optimizing data storage management and ensuring that storage space is used efficiently.

[0038] The present invention also divides the NB-IOT module used for data transmission into two modes: normal and offline. The linkage of the two modes enhances the data synchronization and processing capabilities between platforms. This design can maintain the integrity and availability of data under various network conditions, and enhance the robustness and reliability of the entire security management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the overall structure of the device of the present invention. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0041] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0042] See also Figure 1 , the present invention provides a technical solution:

[0043] A device for collecting and processing redundant data based on NB-iot firefighting Internet of Things alarm data, the specific structure of which includes:

[0044] Data acquisition module, used to divide the target area into equal areas sub-areas, collecting standard data and environmental data with time stamps for each area through fire alarm controllers and electrical fire controllers; the environmental data include temperature, smoke density, circuit current and circuit voltage; the standard data include standard temperature, standard maximum circuit current, standard minimum circuit current, standard maximum circuit voltage and standard minimum circuit voltage;

[0045] Environmental data includes temperature, smoke concentration, circuit current and circuit voltage; multi-dimensional information. These data not only reflect the real-time changes in environmental conditions in the area, but also provide key basis for fire determination and alarm signal generation. At the same time, the electrical fire controller sets the normal value range of current, voltage and temperature.

[0046] The maximum value of the normal range temperature is the standard temperature, and the maximum and minimum values ​​of the normal range of current and voltage are the standard maximum circuit current, standard minimum circuit current, standard maximum circuit voltage, and standard minimum circuit voltage. By comparing environmental data with standard data, abnormal changes in environmental parameters in the target area can be more accurately identified.

[0047] A data transmission module is used to set up an NB-IOT module in the circuit board, and use NB-IOT technology to transmit the standard data and environmental data with timestamps to the fire safety integrated safety management platform for analysis and processing by sending the operator's Internet of Things platform path; the NB-IOT module includes two modes: normal and offline;

[0048] This embodiment adds 10-pin, 20-pin, and 28-pin simple headers to the input and output of the circuit board to intercept data transmitted to the printer. For old fire alarm controllers, the data that the original main board wants to transmit to the printer is intercepted. After the circuit board receives the data, it directly transmits the received data to the receiving printer through the output port, and transmits the received data to the integrated fire safety management platform after pre-processing, without affecting the use of the original printer.

[0049] A security alarm host is also placed in the NB-IOT module. The security alarm host can automatically interact with the security host according to the deployment and withdrawal groups set by the platform. For some security alarm hosts whose deployment and withdrawal cannot be controlled, they are automatically filtered according to the range values ​​set by the platform. In the later stage, pre-processing functions can be developed in a targeted manner according to the actual situation on site.

[0050] If the NB-IOT module is in normal mode, the data will be directly transmitted to the integrated fire and safety management platform for processing; if the NB-IOT module is in offline mode; when the circuit board is abnormally connected to the operator's IoT platform, the alarm data is cached in the TF card as an offline mark. When connected to the operator's IoT platform, a heartbeat packet is used to prompt that there is offline data that needs to be actively requested to be received. When the fire and safety integrated management platform and the operator's IoT platform are offline at the northbound interface, and the alarm data is sent without receiving confirmation from the integrated security platform, the information is automatically written to the TF storage card, and then interacts with the platform through a heartbeat packet to remind the platform that there is offline data that has not been received. The integrated fire and safety management platform actively sends a command to obtain offline package information.

[0051] The initial redundancy processing module is used to copy the environmental data and the standard data into two copies, perform data preprocessing on each category of environmental data, and then perform hash value calculations on the preprocessed data respectively, and form corresponding hash value data sets with the calculation results of each category of data respectively, and map the data values ​​of each category of data in the original data set with the hash values ​​in the hash value data set of the same category of data one by one, and divide each category of hash value data set into corresponding independent hash value data sets and repeated hash value data sets, and map them back to the original data set to form corresponding independent data sets and repeated data sets, and define the data in the corresponding repeated data sets as repeated redundant data, and the fire safety integrated safety management platform will actively shield the repeated redundant data;

[0052] The specific logic for dividing the hash value data set into an independent hash value data set and a repeated hash value data set is: identifying repeated items in the hash value set, taking any one of the repeated items as a representative to form an independent hash value set with other items in the hash value set, and subtracting the repeated item representative to form a repeated hash value set.

[0053] The preprocessing operation is to concatenate the data with the timestamp to form concatenated data, and then convert the concatenated data into binary for representation. The specific logic for obtaining the concatenated data is: converting the timestamp into a Unix timestamp, and concatenating the Unix timestamp with the collected data to obtain the concatenated data; the specific formula for converting the timestamp into a Unix timestamp is:

[0054]

[0055] in, is the Unix timestamp, is the year of the timestamp, is the month of the timestamp, The day of the timestamp, When the timestamp is is the time stamp, The seconds of the timestamp;

[0056] The specific formula for concatenating Unix timestamps and collected data is:

[0057]

[0058] To splice data, is the Unix timestamp, Indicates a connection operation. The environment data and the standard data. In normal hash value processing, it is inevitable that there are cases where the hash values ​​are equal but not duplicate data, which may cause important data to be deleted by mistake. In the process of generating hash values, the timestamp is closely combined with the data content to generate the hash value. This method can effectively avoid the common hash value conflict problem in traditional hash algorithms.

[0059] Add 1 to the end of the concatenated data, then add 0 until the data length reaches 448 bits, and finally add a 64-bit block to the end of the data; select a set of initial hash values, expand the 16 32-bit words represented by the 512-bit block to 64 32-bit words, use 8 working variables to compress the hash value, process it block by block, use the hash value processed by the previous block as the initial value to perform the above compression on the next block, update the hash value until all 64 32-bit words are processed to obtain the final updated hash value, and connect the final updated hash values ​​to obtain the output hash value.

[0060] The initial hash value is the first 32 bits of the binary representation of the decimal part of the square roots of the first 8 prime numbers, and the expansion and compression are both results of data logic processing.

[0061] A data analysis module, for generating a fire generation index by mathematically analyzing independent data sets of environmental data and standard data of each sub-area, performing threshold processing on the fire generation index to obtain a fire index, and generating a fire assessment index according to the fire index of each sub-area;

[0062] The specific logic for generating the fire generation index is: the fire generation index is generated by mathematically analyzing the environmental data and standard data in the independent data sets of each sub-area, and the specific formula is:

[0063]

[0064]

[0065]

[0066] in, For the The fire generation index of each sub-region, is the current deviation, is the voltage deviation, For the The smoke concentration in each sub-area, For the The circuit current in each sub-region, For the The circuit voltage of each sub-region, For the The temperature of the sub-region, is the standard maximum circuit current, is the standard minimum circuit current, is the standard maximum circuit voltage, is the standard minimum circuit voltage, is the standard temperature. Fire generation index for each sub-area Reflects the occurrence of fire in the sub-area. The larger the value, the easier it is to cause a fire. If a fire has already occurred, the higher the intensity of the fire. Smoke density in each sub-area Reflects the smoke density of the sub-area. The larger the value, the higher the smoke density in the area, the easier it is to cause a fire. If a fire has already occurred, the higher the intensity of the fire. Reflects the change of current intensity relative to the normal range. The larger the value, the greater the change of current intensity relative to the normal range; voltage deviation Reflects the change of voltage intensity relative to the normal range. The larger the value, the greater the change of voltage intensity relative to the normal range. It reflects the change of temperature relative to the standard area. The larger the value, the greater the change of temperature relative to the normal range. When a fire occurs, the fire will provide a heat source for the environment and will cause the current and voltage in the corresponding area to increase or decrease abnormally. Therefore, the three can provide an important basis for fire assessment. The larger the data, the easier it is to cause a fire. If a fire has already occurred, the higher the intensity of the fire.

[0067] The specific logic for generating the fire index is: compare the fire generation index with the preset fire threshold, set the fire generation index that is less than or equal to the fire threshold to zero, and subtract the fire threshold from the fire generation index that is greater than the fire threshold; the specific formula is:

[0068]

[0069] in, For the The fire index of each sub-region, For the The fire generation index of each sub-region, is the fire threshold. Fire generation index for each sub-area Reflects the occurrence of fire in the sub-area. The larger the value, the easier it is to cause a fire. If a fire has already occurred, the higher the fire intensity. Fire threshold Is the critical point of fire generation, greater than the fire threshold It means that a fire has occurred. If it is less than the fire threshold It means that there is no fire. Even if the data value is high, it is just fluctuating within the normal range and cannot be used as a basis for judging the fire. Fire index for each sub-area It reflects the impact of the fire on the area. The larger the value, the greater the impact of the fire on the area. Threshold processing can avoid misjudgment of fire assessment results due to a high fire index when no fire occurs.

[0070] The specific logic for generating the fire assessment index is: the fire index of each sub-area is summed to obtain the fire assessment index, and the specific formula is:

[0071]

[0072] in, is the fire assessment index, is the number of areas with a fire index greater than 0, is the total number of regions, is the fire coverage rate, For the Fire index for each sub-area. Fire assessment index Reflects the overall fire situation. The larger the value, the more serious the fire is, and the smaller the value, the milder the fire is. If the fire is small enough, the fire will even be extinguished by the building's own fire-fighting system, without the need to consume additional fire-fighting resources; Fire index for each sub-area Reflects the impact of the fire on the area. The larger the value, the greater the impact of the fire on the area. It reflects the coverage of the area by the fire that has occurred. The larger the value, the higher the fire coverage and the more serious the fire.

[0073] The comprehensive processing module is used to compare the fire assessment index with a preset threshold value, determine the occurrence of the fire according to the comparison result, determine whether the data is invalid redundant data according to the fire occurrence, store non-invalid redundant data in the TF storage card, and automatically delete it after 1 month; directly delete the invalid redundant data.

[0074] The specific logic for defining invalid redundant data is: if the fire assessment index , a fire occurs in the target area, the environmental data and standard data at this time are marked as non-redundant data, and the non-invalid redundant data are stored in the TF storage card and automatically deleted after 1 month; if the fire assessment index , then no fire occurs in the target area, the environmental data and standard data at this time are defined as invalid redundant data, and the invalid redundant data are directly deleted;

[0075] in, is the fire assessment index, Assess threshold values ​​for fire.

[0076] Buildings are equipped with fire protection systems. When the fire is relatively minor, the building's own fire protection system can put out the fire without the need to mobilize other fire protection resources. At this time, the disciplinary data is of low value. The present invention determines the occurrence of the fire by comparing the fire assessment index with the fire assessment threshold; the alarm data that does not require the deployment of additional fire protection resources is defined as invalid data and deleted. This can greatly improve the efficiency of the fire alarm system.

[0077] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0078] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A device for collecting and processing redundant data based on NB-iot firefighting Internet of Things alarm data, characterized in that: The specific structure includes: Data acquisition module, used to divide the target area into equal areas sub-areas, collecting standard data and environmental data with time stamps for each area through fire alarm controllers and electrical fire controllers; the environmental data include temperature, smoke density, circuit current and circuit voltage; the standard data include standard temperature, standard maximum circuit current, standard minimum circuit current, standard maximum circuit voltage and standard minimum circuit voltage; A data transmission module is used to set up an NB-IOT module in the circuit board, and use NB-IOT technology to transmit the standard data and environmental data with timestamps to the fire safety integrated safety management platform for analysis and processing by sending the operator's Internet of Things platform path; the NB-IOT module includes two modes: normal and offline; The initial redundancy processing module is used to copy the environmental data and the standard data into two copies, perform data preprocessing on each category of environmental data, and then perform hash value calculations on the preprocessed data respectively, and form corresponding hash value data sets with the calculation results of each category of data respectively, and map the data values ​​of each category of data in the original data set with the hash values ​​in the hash value data set of the same category of data one by one, and divide each category of hash value data set into corresponding independent hash value data sets and repeated hash value data sets, and map them back to the original data set to form corresponding independent data sets and repeated data sets, and define the data in the corresponding repeated data sets as repeated redundant data, and the fire safety integrated safety management platform will actively shield the repeated redundant data; A data analysis module, for generating a fire generation index by mathematically analyzing independent data sets of environmental data and standard data of each sub-area, performing threshold processing on the fire generation index to obtain a fire index, and generating a fire assessment index according to the fire index of each sub-area; The comprehensive processing module is used to compare the fire assessment index with a preset threshold value, determine the occurrence of the fire according to the comparison result, determine whether the data is invalid redundant data according to the occurrence of the fire, store the non-invalid redundant data in the TF storage card, and automatically delete it after 1 month; directly delete the invalid redundant data; The specific logic for generating the fire generation index is: the fire generation index is generated by mathematically analyzing the environmental data and standard data in the independent data sets of each sub-area, and the specific formula is: in, For the The fire generation index of each sub-region, is the current deviation, is the voltage deviation, For the The smoke concentration in each sub-area, For the The circuit current in each sub-region, For the The circuit voltage of each sub-region, For the The temperature of the sub-region, is the standard maximum circuit current, is the standard minimum circuit current, is the standard maximum circuit voltage, is the standard minimum circuit voltage, is the standard temperature; The specific logic for generating the fire index is: compare the fire generation index with the preset fire threshold, set the fire generation index that is less than or equal to the fire threshold to zero, and subtract the fire threshold from the fire generation index that is greater than the fire threshold; the specific formula is: in, For the The fire index of each sub-region, For the The fire generation index of each sub-region, is the fire threshold; The specific logic for generating the fire assessment index is: The fire index of each sub-area is summed to obtain the fire assessment index, based on the specific formula: in, is the fire assessment index, is the number of areas with a fire index greater than 0, is the total number of regions, is the fire coverage rate, For the Fire index for each sub-area; The specific logic for defining invalid redundant data is: if the fire assessment index , a fire occurs in the target area, the environmental data and standard data at this time are marked as non-redundant data, and the non-invalid redundant data are stored in the TF storage card and automatically deleted after 1 month; if the fire assessment index , then no fire occurs in the target area, the environmental data and standard data at this time are defined as invalid redundant data, and the invalid redundant data are directly deleted; in, is the fire assessment index, Assess threshold values ​​for fire.

2. According to claim 1, a device for collecting and processing redundant data based on NB-iot firefighting Internet of Things alarm data, characterized in that: If the NB-IOT module is in normal mode, the data will be directly transmitted to the integrated fire and safety management platform for processing; if the NB-IOT module is in offline mode; when the circuit board is abnormally connected to the operator's Internet of Things platform, the alarm data will be cached in the TF card as an offline mark. When connected to the operator's Internet of Things platform, a heartbeat packet will be used to prompt that there is offline data and an active request to receive it is required; when the fire and safety integrated management platform and the operator's Internet of Things platform northbound interface are offline, and the alarm data is sent but no confirmation is received from the integrated security platform, the information will be automatically written to the TF storage card, and then the platform will interact with the heartbeat packet to remind the platform that there is offline data that has not been received. The integrated fire and safety management platform will actively send a command to obtain the offline package information.

3. According to claim 1, a device for collecting and processing redundant data based on NB-iot firefighting Internet of Things alarm data, characterized in that: The preprocessing operation is to concatenate the data with the timestamp to form concatenated data, and then convert the concatenated data into binary for representation. The specific logic for obtaining the concatenated data is: convert the timestamp into a Unix timestamp, and concatenate the Unix timestamp and the collected data to obtain the concatenated data; the specific formula for converting the timestamp into a Unix timestamp is: in, is a Unix timestamp, is the year of the timestamp, is the month of the timestamp, The day of the timestamp, When the timestamp is is the time stamp, The seconds of the timestamp; The specific formula for concatenating Unix timestamps and collected data is: To splice data, is a Unix timestamp, Indicates a connection operation. The environmental data and standard data.

4. According to claim 1, a device for collecting and processing redundant data based on NB-iot firefighting Internet of Things alarm data, characterized in that: The hash value of the concatenated data is calculated by the SHA-256 hash function, and the specific logic is as follows: add 1 to the end of the concatenated data, then add 0 until the data length reaches 448 bits, and finally add a 64-bit block to the end of the data; select a set of initial hash values, expand the 16 32-bit words represented by the 512-bit block to 64 32-bit words, compress the hash value using 8 working variables, process each block, use the hash value processed by the previous block as the initial value to perform the above compression on the next block, update the hash value until the 64 32-bit words are processed to obtain the final updated hash value, and concatenate the final updated hash values ​​to obtain the output hash value; The initial hash value is the first 32 bits of the binary representation of the decimal part of the square roots of the first 8 prime numbers, and the expansion and compression are both results of data logic processing.

Images