Fire prevention and emergency integrated platform and emergency communication method based on optical quantum radar

Abstract

The present invention relates to the technical field of flow control of wireless communication networks, and specifically to an integrated fire prevention and emergency platform and an emergency communication method based on optical quantum radar. The present invention first analyzes the transmission stability of data based on the interval between the acquisition time of target class data at the on-site machine and the extraction time of transmission to the host computer within the latest preset period; further, based on the difference characteristics of the target class data and other fire data, combined with the transmission stability, the latest value coefficient of the target class data is obtained, and the value of the target class data is analyzed; further, based on the similarity of the hierarchical transmission of the current target class data to be transmitted and the historical transmission data, combined with the value coefficient, the transmission priority of the data to be transmitted is adjusted, the communication resource utilization is optimized, and the fire prevention and emergency integrated platform is provided with more stable, smooth and valuable fire data.

Description

Technical Field

[0001] The present invention relates to the technical field of flow control of wireless communication networks, and in particular to an integrated fire prevention and emergency communication platform and an emergency communication method based on optical quantum radar. Background Art

[0002] By integrating quantum radar data with multiple sources of information, including drone imagery, satellite remote sensing, and meteorological data, the platform utilizes artificial intelligence algorithms to intelligently identify fire hotspots. Its quantum radar effectively detects flames with an accuracy of up to 10 cm³, with a detection cycle of 0.3 to 36 seconds. It can effectively distinguish fire source types and, combined with meteorological conditions, predict fire spread trends. Leveraging cloud computing and edge computing technologies, the platform rapidly processes massive amounts of data, generating real-time fire data maps and spread paths, providing the command center with an intuitive 3D visualization interface to support informed decision-making.

[0003] Forest fire information data transmitted on the fire emergency platform is mainly transmitted wirelessly. Due to its wide coverage and diverse information sources, limited communication resources need to be reasonably allocated. However, the existing fixed allocation method leads to fixed data transmission priority. Fire scenes are complex and changeable, and the continuity of data streams is easily affected by the fire environment, resulting in a waste of communication resources. At the same time, homogeneous fire data also tends to occupy too many communication resources, which is not conducive to the fire emergency platform to obtain accurate fire dynamics in a timely manner. Summary of the Invention

[0004] In order to solve the problem that the existing fixed data transmission priority is easily affected by the fire environment, resulting in unreasonable allocation of communication resources and affecting the real-time analysis of the fire emergency platform, the purpose of the present invention is to provide an integrated fire emergency platform and emergency communication method based on optical quantum radar. The technical solutions adopted are as follows:

[0005] An emergency communication method for a fire prevention and emergency integrated platform based on optical quantum radar, the method comprising:

[0006] Select any type of fire data as target data; obtain the latest transmission stability of the target data based on the interval between the acquisition time of the target data at the on-site machine and the extraction time of the target data transmitted to the host computer within the latest preset period;

[0007] In the latest preset period, according to the difference characteristics between the target data and other fire data, combined with the transmission stability, the latest value coefficient of the target data is obtained;

[0008] According to the similarity between the hierarchical transmission of the current data to be transmitted and the historical transmission data of the target class data, combined with the value coefficient, the transmission priority of the data to be transmitted is adjusted.

[0009] Furthermore, the method for obtaining the value coefficient includes:

[0010] Convert all the fire data into text vectors; obtain the information richness of the target class data based on similar features between each text vector of the target class data and the text vectors of other types of fire data; the information richness is negatively correlated with the similar features of the text vectors;

[0011] Obtaining a transmission stability coefficient based on a prominent feature of the transmission stability of the target type data compared to the transmission stability of all types of fire condition data; the prominent feature of the transmission stability of the target type data compared to the transmission stability of all types of fire condition data is positively correlated with the transmission stability coefficient;

[0012] The information richness and the transmission stability coefficient are integrated to obtain the latest value coefficient of the target data; the information richness and the transmission stability coefficient are both positively correlated with the value coefficient.

[0013] Furthermore, the method for adjusting the transmission priority of the data to be transmitted includes:

[0014] Arrange the timestamps of each layer of the data to be transmitted in a preset data transmission layer in a time sequence to form a hierarchical structure vector;

[0015] Obtaining a transmission priority coefficient based on similar features between the hierarchical structure vectors of the data to be transmitted and the adjacent historical transmission data, in combination with the length of the hierarchical structure vector of the data to be transmitted and the value coefficient;

[0016] The transmission priority of the data to be transmitted is adjusted according to the transmission priority coefficient.

[0017] Furthermore, the method for adjusting the transmission priority of the data to be transmitted according to the transmission priority coefficient includes:

[0018] When the transmission priority coefficient is greater than or equal to a first preset threshold, the data to be transmitted is determined to be of high priority; when the transmission priority coefficient is less than the first preset threshold and greater than or equal to a second preset threshold, the data to be transmitted is determined to be of medium priority; when the transmission priority coefficient is less than the second preset threshold, the data to be transmitted is determined to be of low priority.

[0019] Furthermore, the method for obtaining transmission stability includes:

[0020] The latest transmission stability of the target class data is obtained according to the average of the intervals between the collection time and the extraction time of each transmission of the target class data; the average of the intervals is negatively correlated with the transmission stability.

[0021] Furthermore, the preset data transmission layer is a TCP / IP protocol stack structure.

[0022] Furthermore, the similarity between vectors is measured by cosine similarity.

[0023] Furthermore, the preset period is 5 minutes in length.

[0024] Furthermore, the fire situation data includes at least temperature, humidity, smoke density and optical images.

[0025] The present invention also proposes an integrated fire prevention and emergency platform based on optical quantum radar, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements any one of the steps of the emergency communication method of the integrated fire prevention and emergency platform based on optical quantum radar.

[0026] The present invention has the following beneficial effects:

[0027] The present invention first selects any type of fire data as target data, which is convenient for category-by-category analysis; further, according to the interval between the target data’s acquisition time at the on-site machine and the extraction time when it is transmitted to the host computer within the latest preset period, the latest transmission stability of the target data is obtained, and the consistency characteristics and transmission stability characteristics of the target data on the data stream are characterized; further, according to the difference characteristics of the target data and other fire data, combined with the transmission stability, the latest value coefficient of the target data is obtained within the latest preset period to characterize the current transmission value of the target data, so as to adjust the transmission priority of the data to be transmitted in the future, provide rich forest fire information for the fire prevention and emergency integration platform, and improve information utilization efficiency; finally, according to the similarity of the hierarchical transmission of the current data to be transmitted and the historical transmission data of the target data, combined with the value coefficient, adjust the transmission priority of the data to be transmitted. The present invention quantifies the transmission value of the data by analyzing the stability of data transmission and the difference relative to other types of data, and adjusts the transmission priority in combination with the hierarchical transmission angle, optimizes the utilization of communication resources, and provides the fire prevention and emergency integration platform with stable, smooth and valuable fire data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 A flowchart of an emergency communication method for an integrated fire prevention and emergency platform based on optical quantum radar provided by one embodiment of the present invention;

[0030] Figure 2 A flowchart of a method for obtaining a value coefficient provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0031] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of an integrated fire prevention and emergency response platform and emergency communication method based on optical quantum radar proposed in accordance with the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0032] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0033] The following describes in detail a fire prevention and emergency integrated platform and an emergency communication method based on optical quantum radar provided by the present invention with reference to the accompanying drawings.

[0034] See also Figure 1 , which shows a flow chart of an emergency communication method for an integrated fire prevention and emergency platform based on optical quantum radar provided by one embodiment of the present invention, specifically including:

[0035] Step S1: Select any type of fire data as target data; obtain the latest transmission stability of the target data based on the interval between the target data collection time at the on-site machine and the extraction time transmitted to the host computer within the latest preset period.

[0036] Long-range optical quantum radars utilize passive phased array optical quantum radar technology. By transmitting and receiving optical quantum signals, they detect fires at long distances based on changes in the physical properties of the quantum quantum, which are correlated with the environment and fire data. For example, the BXRD-100 / 200 / 5000 / 25000 fire radar alarms in the "BXRD" series detect fires ≥10 cm³ within their effective monitoring range at distances of 100 to 25,000 meters. They automatically identify and issue an alarm signal within 0.3 to 36 seconds. Suitable for use in vast forest and grassland environments, they can detect distant fires early and provide early warning, enabling relevant departments to organize timely firefighting and prevention efforts, ensuring fire safety over large areas. Optical quantum radars can detect the specific location and thermal radiation intensity of high-temperature hotspots, such as flames or burning materials. In the early stages of a fire, even a hidden heat source in grasslands can be quickly identified, even through obstructions such as smoke and dust.

[0037] In an embodiment of the present invention, a quantum radar-based integrated fire prevention and emergency response platform improves the accuracy and coverage of information collection through multi-source data fusion (e.g., quantum radar fusion of satellite remote sensing, drone inspections, ground sensors, and manual observation). Edge computing is used to perform real-time data preprocessing and anomaly filtering, reducing redundant data transmission and improving on-site response capabilities. In the data transmission process, multi-path redundancy mechanisms (such as satellite communications, 5G networks, and radio) are employed, combined with a strategy for dynamically adjusting transmission frequency to ensure the real-time delivery of critical fire data. Furthermore, consistency verification, multi-source data cross-comparison, and blockchain technology enable trusted data tracking, ensuring the authenticity and reliability of transmitted data. These techniques are well known to those skilled in the art and will not be elaborated on here.

[0038] The emergency platform combines fire data prediction models and dynamic priority update mechanisms to prioritize updating and displaying core data in key areas based on actual changes in fire data, providing accurate and timely decision-making support for emergency response.

[0039] In one embodiment of the present invention, the basic transport layer of the integrated fire prevention and emergency response platform utilizes the TCP / IP protocol. Specifically, the data transmission layer is pre-configured as a TCP / IP protocol stack, comprising a network interface layer, a network layer, a transport layer, and an application layer. Detection equipment, such as data loggers and recorders, are referred to as field devices; a monitoring center device that displays data is referred to as the host computer. The field devices collect various fire data in real time, including at least temperature, humidity, smoke concentration, and optical images. The frequency of data collection is determined by the equipment itself, with a preset transmission frequency of 30 seconds.

[0040] Among them, programmable controllers, industrial computers, single-chip microcomputers, etc. that collect data from various instruments are referred to as data loggers; fire data all contain geographical locations, such as the three-dimensional coordinates (longitude, latitude, and altitude) corresponding to the fire data, which facilitates the fire prevention and emergency integrated platform to analyze the fire situation.

[0041] First, any type of fire data is selected as the target data to facilitate analysis by category; considering that the complex environment of the fire affects the transmission stability of a certain type of fire data, the fire data may be interrupted or delayed in transmission, which will be directly reflected in the interval between the data collection time at the on-site machine and the extraction time at the host computer. Therefore, according to the interval between the collection time of the target data at the on-site machine and the extraction time of the data transmitted to the host computer within the latest preset period, the latest transmission stability of the target data is obtained to characterize the continuity characteristics and transmission stability characteristics of the target data in the data stream.

[0042] Preferably, in one embodiment of the present invention, considering that the target class data has collected and transmitted data multiple times in the recent period, the shorter the average interval between the collection time and the extraction time of the target class data, the shorter the time consumed for data transmission, the fewer various transmission anomalies such as intermittent 5G signal transmission, and the stronger the transmission stability, so within the latest preset period, the latest transmission stability of the target class data is obtained based on the average of the interval between the collection time and the extraction time of each transmission of the target class data; the mean of the interval is negatively correlated with the transmission stability.

[0043] As an example, the length of the preset cycle is 5 minutes; the calculation formula for transmission stability includes:

[0044] ;

[0045] Among them, i represents the type number of the target data; represents the transmission stability of the i-th target class data in the latest preset period; j represents the sequence number of transmission times; Indicates the total number of times the target data of type i is transmitted in the latest preset period; Indicates the timestamp of the collection time of the jth transmission data of the i-th target class data within the latest preset period; Indicates the timestamp of the extraction time of the jth transmission data of the i-th target class data within the latest preset period; Indicates taking the absolute value; Represents an exponential function with the natural constant e as its base.

[0046] In the calculation formula of transmission stability, the acquisition time and extraction time are expressed in the form of timestamps, and the absolute value of the difference is used to express the time. It represents the interval between moments; the overall time interval of the latest preset cycle is expressed by taking the mean of the time intervals. The smaller the overall time interval, the fewer transmission anomalies, the smaller the impact, the stronger the continuity of the data stream, and the stronger the transmission stability. Then, negative correlation mapping is performed through exp(-x), and the logical relationship is adjusted to obtain transmission stability; where transmission stability is a specific value, and x represents the independent variable.

[0047] Step S2: Within the latest preset period, based on the difference characteristics between the target data and other fire data and combined with the transmission stability, the latest value coefficient of the target data is obtained.

[0048] Taking into account the homogeneous fire data in the same period, it is difficult to meet the information integration needs of the fire emergency integration platform, and it is easy to occupy too many communication transmission resources, resulting in transmission delays or losses of high-value data. Therefore, in the latest preset period, according to the difference characteristics between the target data and other fire data, the homogeneity of the target data is analyzed, and the transmission stability representing the continuity of the data stream is combined to obtain the latest value coefficient of the target data, characterize the current transmission value of the target data, and provide rich forest fire information for the fire emergency integration platform in the future. Improve the efficiency of information utilization.

[0049] Preferably, in one embodiment of the present invention, see Figure 2 , which shows a flow chart of a method for obtaining a value coefficient provided by an embodiment of the present invention, specifically comprising:

[0050] Step S201: convert all fire data into text vectors; obtain the information richness of the target class data based on the similarity between each text vector of the target class data and the text vectors of other types of fire data.

[0051] Considering that different types of fire data are difficult to directly compare and analyze, all fire data are first converted into text vectors. Specifically, for pure text data, they can be converted into text vectors through the BERT (Bidirectional Encoder Representations from Transformers) model; for structured data such as tabular data, the field values ​​of each row can be spliced ​​into a text string, and then vectorized using the BERT model; for image data, an image description model (such as CNN+LSTM) is used to generate a text description of the image, and then the description is vectorized; for time series temperature data, the time series data can be converted into text descriptions, and then the BERT model is used to generate text vectors based on the text descriptions. For example, if the temperature time series data is [20, 21, 22, 23, 24], the text description is "the temperature gradually rises from 20 degrees to 24 degrees". These are all technical means well known to those skilled in the art and will not be repeated here.

[0052] As an example, considering that the greater the cosine similarity between vectors, the more similar the vectors are, the similarity features between vectors are measured by cosine similarity; considering that the more similar the text vector of the target class data is to the text vector of other types of data, the more likely it is that the data is homogeneous fire data containing similar information, which cannot provide rich fire information for the integrated fire prevention and emergency platform, the information richness is negatively correlated with the similarity features of the text vectors.

[0053] The calculation formula for information richness includes:

[0054] ;

[0055] Among them, i represents the type number of the target data; Indicates the information richness of the i-th target class data in the latest preset period; represents an exponential function with the natural constant e as the base; It represents the mean of the cosine similarity between the text vector of the i-th target class data and the text vectors of other fire class data in the latest preset period.

[0056] In the calculation formula of information richness, the text vector of the i-th target class data is combined with the text vectors of other fire data in pairs, and the cosine similarity of each combination is calculated and the average is taken to obtain When calculating the cosine similarity, the shorter text vectors are padded with zeros to ensure that the number of dimensions of the vectors in the combination is the same; negative correlation mapping is performed through exp(-x) to adjust the logical relationship to obtain information richness.

[0057] Step S202: Obtain a transmission stability coefficient based on the prominent characteristics of the transmission stability of the target type data compared with the transmission stability of all types of fire situation data.

[0058] Considering that the more prominent the transmission stability of target data is and the stronger the transmission stability is, the more it can provide continuous and stable fire data for the fire emergency integrated platform, the transmission stability of target data is positively correlated with the transmission stability coefficient compared with the prominent characteristics of the transmission stability of all types of fire data.

[0059] As an example, within the latest preset period, the transmission stability of the target class data is used as the numerator, and the mean of the transmission stability of all types of fire data is used as the denominator. The prominent characteristics of the transmission stability are expressed in the form of a ratio, and the fractional ratio is used as the latest transmission stability coefficient of the target class data.

[0060] Step S203: Integrate the information richness and transmission stability coefficient to obtain the latest value coefficient of the target class data.

[0061] After evaluating the value of the target data to the fire emergency integrated platform from the perspective of information richness and transmission stability, the information richness and transmission stability coefficients can be integrated to obtain the latest value coefficient of the target data.

[0062] As an example, considering that the better the continuity of the data flow, the less the equipment working status and data transmission are affected by the complex fire environment, and the greater the information richness, the more stable and information-rich fire data can be provided to the fire emergency integrated platform, and the higher the value in the platform information integration, the information richness and transmission stability coefficient are positively correlated with the value coefficient.

[0063] Specifically, the product of the latest transmission stability coefficient of the target data and the information richness is linearly normalized, and the normalized result is used as the value coefficient of the target data in the latest preset period, providing a basis for subsequent adjustment of the transmission priority.

[0064] Step S3: According to the similarity of the hierarchical transmission of the current target data to be transmitted and the historical transmission data, combined with the value coefficient, the transmission priority of the target data to be transmitted is adjusted.

[0065] Taking into account that the data transmission process is affected not only by the fluency of the data acquired itself, but also by the data transmission structure, the data transmission between data transmission layers is mainly realized in the form of data messages, and its intermittent message structure will affect the fluency of the terminal host computer to obtain data. Therefore, according to the similarity of the hierarchical transmission of the current data to be transmitted and the historical transmission data of the target type data, the smooth characteristics of data transmission are reflected from the perspective of hierarchical transmission. Combined with the value coefficient, the transmission priority of the data to be transmitted is adjusted, and the utilization of communication resources is optimized to provide stable, smooth and valuable fire data for the fire emergency integrated platform.

[0066] Preferably, in one embodiment of the present invention, the timestamps of each layer path of the data to be transmitted in the preset data transmission layer are sorted in chronological order to form a hierarchical structure vector, which represents the transmission smoothness characteristics of the data to be transmitted; considering that the hierarchical structure vectors of two adjacent transmissions are similar, it means that the processing time of the data at each layer is relatively stable, and there is no obvious fluctuation or congestion during the network transmission process. The smoother the transmission, the greater the value coefficient, which means that the data to be transmitted is more important to the fire emergency integrated platform;

[0067] Based on this, the transmission priority coefficient is obtained according to the similarity characteristics of the hierarchical structure vectors of the data to be transmitted and the adjacent historical transmission data, combined with the length of the hierarchical structure vector of the data to be transmitted and the value coefficient;

[0068] As an example, the calculation formula of the transmission priority coefficient includes:

[0069] ;

[0070] Where i represents the type number of the target data; k represents the number of transmission times of the target data to be transmitted; The transmission priority coefficient of the data to be transmitted with the sequence number k of the i-th target class data; represents the linear normalization function; Represents the latest value coefficient of the i-th target class data; The hierarchical structure vector representing the data to be transmitted with sequence number k of the i-th target class data; The hierarchical structure vector of the data to be transmitted with the sequence number k-1 representing the i-th target class data; represents the cosine similarity function; Indicates the length of the hierarchical structure vector of the data to be transmitted with sequence number k for the i-th target class data.

[0071] In the calculation formula of the transmission priority coefficient, the cosine similarity is used to express the similarity characteristics of the hierarchical structure vectors of the data to be transmitted and the adjacent historical transmission data, thereby expressing the similarity of the hierarchical transmission of the current data to be transmitted and the historical transmission data of the target class data. The closer the cosine similarity is to 1, The larger the value coefficient is, the more similar the two hierarchical structure vectors are. From the perspective of hierarchical transmission, it reflects that the data transmission is smoother. At the same time, the greater the value coefficient is, the higher the value is for the fire emergency integration platform. The more it can provide smooth and valuable fire data for the fire emergency integration platform, the greater the transmission priority coefficient is. In addition, the transmission paths of different hierarchical structure vectors may be different and have different lengths. Therefore, with the help of Remove the effect of vector length; finally Perform linear normalization and multiply by a constant 100 to The value range of is adjusted to [0, 100] to facilitate subsequent adjustment of transmission priority.

[0072] The transmission priority of the data to be transmitted is further adjusted according to the transmission priority coefficient.

[0073] As an example, when the transmission priority coefficient is greater than or equal to the first preset threshold, the data to be transmitted is determined to be of high priority; when the transmission priority coefficient is less than the first preset threshold and greater than or equal to the second preset threshold, the data to be transmitted is determined to be of medium priority; when the transmission priority coefficient is less than the second preset threshold, the data to be transmitted is determined to be of low priority.

[0074] Specifically, the first preset threshold is 80, and the second preset threshold is 50. The weighted round-robin (WRR) algorithm is mainly used, and the dynamic priority queue algorithm is supplemented. The weighted round-robin algorithm and the dynamic priority queue algorithm are already existing technologies for adjusting transmission resources. Here, only a brief description is given:

[0075] High-priority data has a weight of 3, medium-priority data has a weight of 2, and low-priority data has a weight of 1. Within each scheduling cycle, transmission opportunities are allocated based on the weight ratio: high-priority data has 3 transmission opportunities, medium-priority data has 2 transmission opportunities, and low-priority data has 1 transmission opportunity. For example, HPQ → HPQ → HPQ → MPQ → MPQ → LPQ, where HPQ stands for high-priority queue, MPQ stands for medium-priority queue, and LPQ stands for low-priority queue.

[0076] For high-priority data, at least 40% of the total bandwidth is reserved (e.g., 400Mbps for a total bandwidth of 1Gbps). When high-priority data arrives, the transmission of low-priority data is immediately interrupted. The transmission method is real-time transmission, sent in single packets, and merging or batch processing is prohibited.

[0077] For medium-priority data, the policy is dynamically adjusted: Normal mode: Bandwidth is allocated based on weight, allowing 50% of the remaining bandwidth to be occupied (for example, 300 Mbps when 600 Mbps is available). Bandwidth-constrained mode: When the high-priority queue backlog exceeds the threshold (for example, queue length > 1000 packets), the medium-priority weight is temporarily reduced to 1. When the network load is < 60%, the weight is increased to 3. The transmission method is set to small batch transmission (for example, 10 packets per batch).

[0078] For low-priority data, delayed transmission and batch transmission are used: data packets are delayed for at least 200ms before transmission, and multiple small packets are merged into a single large packet (for example, 50 data packets per batch); the maximum occupied bandwidth is set to no more than 10% of the total bandwidth (for example, the limit is 100Mbps when 1Gbps), and it can be set to: when the high / medium priority queue is not empty, low-priority data transmission is suspended.

[0079] Dynamic priority queue adjustment mechanism: Real-time monitoring of link utilization (e.g., sampling every 5 seconds). When the link load is low (utilization less than 50%), the weights of medium and low priority packets are increased (MPQ+1, LPQ+1, HPQ→HPQ→HPQ→MPQ→MPQ→MPQ→LPQ→LPQ). When the link load is high, only high-priority data is allowed to transmit.

[0080] It should be noted that the fire data of the kth target class data has not completed the entire transmission process after passing through the preset data transmission layer, so it is still called data to be transmitted; in another embodiment of the present invention, the implementer can also adjust the transmission priority of the data to be transmitted that has not yet started to be transmitted based on the similarity between the hierarchical transmission of the fire data of the target class data transmitted for the last time and the adjacent historical transmission data, combined with the value coefficient.

[0081] An embodiment of the present invention also provides an integrated fire prevention and emergency platform based on optical quantum radar, which includes a memory, a processor and a computer program, wherein the memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs in the processor, it can implement the emergency communication method of the integrated fire prevention and emergency platform based on optical quantum radar described in steps S1-S3.

[0082] In summary, in order to solve the problem that the existing fixed data transmission priority is easily affected by the fire environment, resulting in unreasonable allocation of communication resources and affecting the real-time analysis technology of the fire emergency platform, the present invention proposes a fire emergency integrated platform and emergency communication method based on optical quantum radar. The present invention first analyzes the transmission stability of the data according to the interval between the acquisition time of the target class data at the on-site machine and the extraction time of the target class data transmitted to the host computer within the latest preset period; further, based on the difference characteristics of the target class data and other fire data, combined with the transmission stability, the latest value coefficient of the target class data is obtained, and the value of the target class data is analyzed; further, based on the similarity of the hierarchical transmission of the current target class data to be transmitted and the historical transmission data, combined with the value coefficient, the transmission priority of the data to be transmitted is adjusted, the communication resource utilization is optimized, and the fire emergency integrated platform is provided with more stable, smooth and valuable fire data.

[0083] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. An emergency communication method for a fire prevention and emergency integrated platform based on optical quantum radar, characterized in that: The method comprises: Select any type of fire data as target data; obtain the latest transmission stability of the target data based on the interval between the acquisition time of the target data at the on-site machine and the extraction time of the target data transmitted to the host computer within the latest preset period; In the latest preset period, according to the difference characteristics between the target data and other fire data, combined with the transmission stability, the latest value coefficient of the target data is obtained; Adjusting the transmission priority of the data to be transmitted based on the similarity between the hierarchical transmission of the current data to be transmitted and the historical transmission data of the target class data and the value coefficient; The method for adjusting the transmission priority of the data to be transmitted includes: Arrange the timestamps of each layer of the data to be transmitted in a preset data transmission layer in a time sequence to form a hierarchical structure vector; Obtaining a transmission priority coefficient based on similar features between the hierarchical structure vectors of the data to be transmitted and the adjacent historical transmission data, in combination with the length of the hierarchical structure vector of the data to be transmitted and the value coefficient; The transmission priority of the data to be transmitted is adjusted according to the transmission priority coefficient.

2. The emergency communication method of the fire prevention and emergency integrated platform based on optical quantum radar according to claim 1 is characterized in that: The method for obtaining the value coefficient includes: Convert all the fire data into text vectors; obtain the information richness of the target class data based on similar features between each text vector of the target class data and the text vectors of other types of fire data; the information richness is negatively correlated with the similar features of the text vectors; Obtaining a transmission stability coefficient based on a prominent feature of the transmission stability of the target type data compared to the transmission stability of all types of fire condition data; the prominent feature of the transmission stability of the target type data compared to the transmission stability of all types of fire condition data is positively correlated with the transmission stability coefficient; The information richness and the transmission stability coefficient are integrated to obtain the latest value coefficient of the target data; the information richness and the transmission stability coefficient are both positively correlated with the value coefficient.

3. The emergency communication method of the fire prevention and emergency integrated platform based on optical quantum radar according to claim 1 is characterized in that: The method for adjusting the transmission priority of the data to be transmitted according to the transmission priority coefficient includes: When the transmission priority coefficient is greater than or equal to a first preset threshold, the data to be transmitted is determined to be of high priority; when the transmission priority coefficient is less than the first preset threshold and greater than or equal to a second preset threshold, the data to be transmitted is determined to be of medium priority; when the transmission priority coefficient is less than the second preset threshold, the data to be transmitted is determined to be of low priority.

4. The emergency communication method of the fire prevention and emergency integrated platform based on optical quantum radar according to claim 1 is characterized in that: The method for acquiring transmission stability includes: The latest transmission stability of the target class data is obtained according to the average of the intervals between the collection time and the extraction time of each transmission of the target class data; the average of the intervals is negatively correlated with the transmission stability.

5. The emergency communication method of the fire prevention and emergency integrated platform based on optical quantum radar according to claim 1 is characterized in that: The preset data transmission layer is a TCP / IP protocol stack structure.

6. The emergency communication method of the fire prevention and emergency integrated platform based on optical quantum radar according to claim 2 is characterized in that: The similarity between vectors is measured by cosine similarity.

7. The emergency communication method of the fire prevention and emergency integrated platform based on optical quantum radar according to claim 1 is characterized in that: The duration of the preset cycle is 5 minutes.

8. The emergency communication method of the fire prevention and emergency integrated platform based on optical quantum radar according to claim 1 is characterized in that: The fire condition data includes at least temperature, humidity, smoke density and optical images.

9. An integrated fire prevention and emergency platform based on optical quantum radar, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the emergency communication method of the fire prevention and emergency integrated platform based on optical quantum radar are implemented as described in any one of claims 1 to 8.

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