Rescue equipment configuration method and system of emergency rescue vehicle

Abstract

The invention discloses a rescue equipment configuration method and system of an emergency rescue vehicle, and relates to the field of disaster emergency rescue. The method is applied to an emergency rescue platform and comprises the following steps: acquiring basic information of an emergency event; carrying out keyword extraction and standardization processing on the basic information of the emergency event to obtain a plurality of event attributes of the emergency event; inputting the plurality of event attributes into a preset emergency knowledge graph to obtain a plurality of associated attributes; generating a rescue equipment table of a plurality of associated attributes and a plurality of event attributes; and integrating the plurality of rescue equipment tables to obtain a rescue equipment configuration scheme of the emergency. By implementing the technical scheme provided by the invention, the problem that an existing rescue equipment list cannot provide a reasonable rescue equipment configuration scheme for a novel combined event is solved.

Description

Technical Field

[0001] The present application relates to the technical field of disaster emergency rescue, and in particular to a rescue equipment configuration method and system for an emergency rescue vehicle. Background Art

[0002] With the development of information technology, when an emergency occurs, we can use communication tools to quickly understand the basic situation of the emergency, so that we can take corresponding emergency measures in time to protect the lives and property of residents.

[0003] However, in real life, emergencies are often sudden and unpredictable, which requires rescue teams to set out in a very short time after receiving the mission, resulting in insufficient time to evaluate and analyze the incident, which in turn leads to incomplete configuration of rescue equipment and omission of some key equipment. At present, in order to respond to these emergencies in a timely manner, a standardized rescue equipment list is established in advance. When an emergency occurs, the corresponding rescue equipment is queried from the rescue equipment list according to the type, time, location, and scale of the emergency to complete the rapid configuration of the rescue equipment. However, the above rescue equipment list is established based on currently known emergency events. For some new emergency events or combined events, the rescue equipment list may not have targeted equipment configuration solutions, resulting in unreasonable configuration of the rescue equipment. Summary of the invention

[0004] In order to solve the problem that the existing rescue equipment list cannot provide a reasonable rescue equipment configuration solution for new combination events, the present application provides a rescue equipment configuration method and system for an emergency rescue vehicle.

[0005] In a first aspect, the present application provides a method for configuring rescue equipment of an emergency rescue vehicle, which is applied to an emergency rescue platform, and the method comprises: Get basic information about emergency events; Perform keyword extraction and standardization processing on the basic information of the emergency event to obtain multiple event attributes of the emergency event; Inputting a plurality of the event attributes into a preset emergency event knowledge graph to obtain a plurality of associated attributes; generating a rescue equipment table of a plurality of the association attributes and a plurality of the event attributes; A plurality of the rescue equipment tables are integrated to obtain a rescue equipment configuration plan for the emergency event.

[0006] Optionally, the basic information of the emergency event is parsed to obtain a plurality of sub-information of the emergency event and information sources corresponding to each of the plurality of sub-information; Classifying the plurality of sub-information according to information sources corresponding to the plurality of sub-information to obtain a plurality of classification clusters; The keyword extraction algorithms corresponding to the multiple classification clusters are used to extract the keywords of the multiple classification clusters.

[0007] Optionally, the extracting keywords of the multiple classification clusters by using keyword extraction algorithms corresponding to the multiple classification clusters specifically includes: Acquire multiple non-keywords of a first classification cluster, where the first classification cluster is any one of the multiple classification clusters; Screening out the plurality of non-keywords to obtain a plurality of non-keywords to be verified; Performing part-of-speech tagging on the plurality of non-keywords to be verified; Based on the parts of speech of the multiple non-keywords to be verified, the multiple non-keywords to be verified are assigned to a second classification cluster for keyword verification to extract missing keywords from the multiple non-keywords to be verified. The second classification cluster is any one of the multiple classification clusters except the first classification cluster.

[0008] Optionally, obtaining a first association attribute between a plurality of the event attributes; Inputting a plurality of the first associated attributes into the emergency knowledge graph to generate a plurality of second associated attributes; Calculate convergence values ​​of a plurality of the second association attributes in the emergency event knowledge graph; If the convergence value reaches a threshold, it is determined that the plurality of first association attributes and the plurality of second association attributes are association attributes of the plurality of event attributes.

[0009] Optionally, if the convergence value reaches a threshold, determining that the plurality of first association attributes and the plurality of second association attributes are association attributes of the plurality of event attributes specifically further includes: Matching multiple event attributes of the emergency event with a historical emergency event database to obtain multiple similar historical emergency events; Calculating similarity values ​​between the emergency event and a plurality of similar historical emergency events; If the similarity value between the emergency event and the first similar historical emergency event is greater than or equal to a preset value, the convergence value threshold of the first similar historical emergency event is used as the threshold of the emergency event, and the first similar historical emergency event is any one of the multiple similar historical emergency events.

[0010] Optionally, after calculating the similarity values ​​between the emergency event and the plurality of similar historical emergency events, the method further includes: If the similarity values ​​between the plurality of similar historical emergency events and the emergency event are all less than a preset value, then reading the complexity and the number of iterations of the emergency event; Based on the complexity of the emergency event and the number of iterations, a convergence value threshold of the emergency event is adjusted.

[0011] Optionally, a functional evaluation is performed on the plurality of rescue equipment tables to obtain functional adaptability of the plurality of rescue equipment tables; Based on the functional adaptability of the plurality of rescue equipment tables, a rescue equipment configuration plan for the emergency event is generated.

[0012] In a second aspect, the present application provides a rescue equipment configuration system for an emergency rescue vehicle, wherein the system is an emergency rescue platform, and the emergency rescue platform includes an acquisition module and a processing module, wherein: The acquisition module is used to acquire basic information of emergency events; The processing module is used to perform keyword extraction and standardization processing on the basic information of the emergency event to obtain multiple event attributes of the emergency event; Inputting a plurality of the event attributes into a preset emergency event knowledge graph to obtain a plurality of associated attributes; Generate a plurality of rescue equipment tables of the associated attributes and the event attributes; integrate the plurality of rescue equipment tables to obtain a rescue equipment configuration plan for the emergency event.

[0013] In a third aspect, the present application provides an electronic device comprising a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a method as described in any one of the first aspects.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the first aspects is executed.

[0015] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. For the new emergency events and combined events that are currently occurring, this application splits the basic information of these emergency events into multiple event attributes, and then inputs the multiple event attributes into a pre-established emergency event knowledge graph to query multiple historical event attributes that are similar or identical to the current multiple event attributes. At this time, the multiple historical event attributes are not from the same historical emergency event. Then, based on the associated attributes of the historical event attributes, the associated attributes corresponding to the multiple event attributes of the current emergency event are determined, so as to mine the risk information that may be ignored by the current emergency event, and finally generate the rescue equipment configuration plan for the current emergency event based on the information provided by the associated attributes and event attributes. Compared with the traditional rescue equipment list, this plan can better adapt to complex and changeable emergency events and make the configuration plan of the rescue equipment more reasonable.

[0016] 2. In the process of splitting the basic information of emergency events, since the composition of the basic information comes from different information channels, if a unified information processing method is used, the basic information splitting will be inaccurate. Therefore, this application performs cluster analysis on the basic information, divides the basic information into multiple classification clusters, and adopts targeted information processing algorithms in each classification cluster to improve the accuracy of basic information splitting. In the process of processing information, the classification cluster will cause some key information to be divided into non-key information due to different description methods; therefore, when performing cluster analysis, this application also divides the non-key information output by each classification cluster into other classification clusters for processing, so that other classification clusters re-verify whether the non-key information is key information, thereby ensuring the integrity of the basic information splitting.

[0017] 3. When querying the associated attributes of emergency events in the emergency knowledge graph, it is difficult to ensure the comprehensiveness of the associated attributes by relying on only one query; therefore, this application performs deep mining through iterative query to dig out some risk information hidden in emergency events. In addition, in order to prevent unlimited iteration, this application dynamically sets the convergence value threshold of each iteration according to the complexity of the emergency event and the number of iterations, so as to adapt to the mining depth and breadth of different emergency events. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a method for configuring rescue equipment of an emergency rescue vehicle provided in an embodiment of the present application.

[0019] Figure 2 It is a structural schematic diagram of a rescue equipment configuration system for an emergency rescue vehicle provided in an embodiment of the present application.

[0020] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0021] Explanation of the reference numerals: 1. Acquisition module; 2. Processing module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 505. Memory. DETAILED DESCRIPTION

[0022] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0023] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0024] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0025] Emergency events happen from time to time in my country. These emergency events include natural disasters (earthquakes, forest fires, mudslides, etc.) and man-made disasters (factory fires, car accidents, building collapses, etc.); but thanks to today's advanced information technology, even if you are thousands of miles away from the scene of the emergency, you can still get to know the relevant situation of the emergency in the first place. However, the occurrence of emergency events is often sudden and unpredictable. For example, for sudden factory fires, as the rescue time is prolonged, it may cause an explosion, which will further increase the harm of the emergency. Therefore, this also requires the rescue team to set off in a very short time after receiving the mission, but this will result in insufficient time to evaluate and analyze the incident, which will lead to incomplete configuration of rescue equipment, omission of some key equipment, and increase the difficulty of rescue.

[0026] At present, in order to quickly respond to these emergencies, an event file will be established in advance based on the emergency events that have been handled in the past. Then, according to the basic information described in the event file, research and discussion will be conducted on how to configure rescue equipment when similar emergency events occur in the future. Finally, the discussion results will be formulated into a rescue equipment list. Once a rescue mission is received, the rescue equipment configuration plan suitable for this rescue operation will be queried from the rescue equipment list based on the known situation of this rescue mission, so as to quickly complete the configuration of the rescue equipment and go to the emergency area in time to carry out rescue.

[0027] However, the above rescue equipment list is established based on currently known emergency events. For some new emergency events or combined events, the rescue equipment list may not have targeted equipment configuration solutions, resulting in unreasonable configuration of the rescue equipment.

[0028] In order to solve the above problems, the present application provides a method for configuring rescue equipment of an emergency rescue vehicle, which is applied to an emergency rescue platform, such as Figure 1 As shown, the method includes steps S101 to S105, which are as follows: S101. Obtain basic information of the emergency event.

[0029] In the above steps, if an emergency occurs in a certain place, the emergency rescue platform will collect relevant information of the emergency through various information channels, and then perform pre-processing operations such as format conversion, deduplication, screening and verification on the collected relevant information to generate basic information of the emergency. Among them, the various information channels include descriptions of eyewitnesses, news reports, social media and official notifications, and the relevant information of the emergency includes voice, text, images and videos describing the emergency.

[0030] S102: extract keywords and perform standardization processing on basic information of the emergency event to obtain multiple event attributes of the emergency event.

[0031] In the above steps, the basic information of the emergency event contains multiple types of event information, and different types of event information have different accuracy. It can be understood that the video has the highest accuracy, followed by images, text, and voice. The lower the accuracy, the more interference, and the higher the accuracy, the less interference. Based on this, the present application performs cluster analysis on different types of information, and adopts a corresponding keyword extraction algorithm for each cluster cluster, so as to extract effective and accurate data from the basic information of the emergency event. Specifically: first, the basic information of the emergency event is parsed to obtain multiple sub-information of the emergency event and the information sources of the multiple sub-information; then, according to the information sources of the multiple sub-information, the multiple sub-information is classified to obtain multiple classification clusters. In the present application, the multiple classification clusters include voice classification clusters, text classification clusters, image classification clusters, and video classification clusters, wherein the same classification cluster may contain multiple sub-information, for example, the sub-information in the voice classification cluster may come from different on-site eyewitness descriptions or from news reporters' reports; then, the keyword extraction algorithms corresponding to each classification cluster are adopted to extract keywords of multiple classification clusters.

[0032] Among them, for the speech classification cluster, speech recognition technology combined with sentiment analysis can be used to extract keywords. Speech recognition technology converts speech into text, and then sentiment analysis is performed on the speech corresponding to the text. For example, when performing sentiment analysis, the tone, coherence and speech speed can be used to determine what the describer wants to emphasize, so as to clarify the key information in the speech information.

[0033] For text classification clusters, the word frequency-inverse document frequency technology combined with the text sorting algorithm can be used to extract keywords. The word frequency-inverse document frequency technology extracts fuzzy keywords from the document, and then the text sorting algorithm is used to verify the fuzzy keywords to obtain accurate keywords. Specifically, the text is segmented to obtain multiple words, and then the word frequency and inverse document frequency of multiple words are calculated. The word frequency reflects the frequency of the word in the current document, and the inverse document frequency reflects the rarity of a word in multiple document collections. It can be understood that if a word appears frequently in a document, it means that the word is more important in the document. If a word does not appear frequently in multiple document collections, it means that the word has a strong ability to distinguish and is more important for determining the topic of the document. However, considering word frequency or inverse document frequency alone has certain limitations. By calculating the product of word frequency and inverse document frequency, the importance value of each word is obtained. The higher the importance value, the more critical the word is, and thus multiple fuzzy keywords of the text classification cluster can be determined. At this time, the fuzzy keywords obtained by using the word frequency-inverse document frequency technology ignore the semantic relationship between words and the structural information of the text. Therefore, the fuzzy keywords need further verification before they can be determined as accurate keywords. The text sorting algorithm constructs a relationship graph between words, taking into account the contextual relationship and co-occurrence of words in the text, and can capture the semantic connection between words. Specifically, a range window is first used to construct a vocabulary relationship graph, and the fuzzy keywords are nodes in the vocabulary relationship graph. For example, if two fuzzy keywords appear in a 5-byte range window of the document at the same time, it means that the two fuzzy keywords are associated, that is, an associated edge is constructed between the two fuzzy keywords in the vocabulary relationship graph; then an iterative algorithm is used to calculate and update the weight value of each node in the vocabulary relationship graph. When the weight value of each node converges, the node with a higher weight value is selected as the accurate keyword.

[0034] For image classification clusters, target detection technology combined with image annotation technology can be used to extract keywords. Target detection technology identifies entities related to emergency events such as people, vehicles, fires, and floods in the image, and then image annotation technology adds labels to objects and scenes in the image. Finally, these labels are processed into text to extract keywords.

[0035] For video classification clusters, since videos contain voice, text and images, when extracting keywords from video classification clusters, the algorithms in voice classification clusters, text classification clusters and image classification clusters can be combined to extract keywords. The specific processing method has been described in the above document and will not be elaborated on here.

[0036] In a possible implementation, specific information types are clustered in each classification cluster, and there are differences in the information expression methods of different information types, resulting in different presentation methods for the same concept. For example, the image and video classification clusters can more intuitively display certain scenes or objects, and the text classification cluster can provide more detailed background information and descriptions. However, this differential expression method easily causes keywords in a certain classification cluster to be classified as non-keywords, thus omitting some keywords. For example, an object that appears in an image or video is regarded as a non-keyword because it lacks detailed context description. To solve this problem, this application further verifies the non-keywords in each classification cluster to improve the integrity of keyword extraction. Specifically: First, duplicate words are removed and stop words are screened from the non-keywords in multiple classification clusters to obtain multiple non-keywords to be verified, where the stop words include words such as "le", "de", "zai", etc.; then, part-of-speech tagging is performed on the multiple non-keywords to be verified, that is, the part-of-speech of the non-keywords to be verified is tagged as one of the parts of speech such as adjectives, nouns, verbs, etc.; then, according to the part-of-speech of the non-keywords to be verified, the part-of-speech of the non-keywords to be verified is sequentially input into multiple classification clusters for keyword verification. It can be understood that the non-keywords to be verified will not be input into the original classification cluster again; if the non-keyword to be verified is defined as a keyword in a certain classification cluster, the non-keyword to be verified is output as a keyword. In the process of sequentially inputting the part-of-speech of the non-keywords to be verified into multiple classification clusters for verification, to improve the verification efficiency and reduce unnecessary processing steps, according to the hit accuracy rates of each of the multiple classification clusters for keywords of different parts of speech, the input verification order of the non-keywords to be verified is determined. For example, if the video classification cluster has a higher hit accuracy rate for adjectives, the non-keyword to be verified with the part-of-speech of an adjective is preferentially input into the video classification cluster for verification, thereby improving the verification efficiency.

[0037] Finally, after completing the keyword extraction of the emergency event, the keywords need to be standardized to obtain the event attributes of the emergency event; here, the standardization process is to convert heterogeneous data into homogeneous data and convert the expression method into a standard format to ensure more accurate and efficient subsequent processing.

[0038] S103. Input multiple event attributes into a preset emergency event knowledge graph to obtain multiple associated attributes.

[0039] In the above steps, the pre-constructed emergency event knowledge graph contains the association relationship between the event attributes of various types of emergency events. Therefore, when the emergency event is a new type of emergency event or a combined event composed of multiple emergency events, by inputting the event attributes of the emergency event into the preset emergency event knowledge graph, and then according to the association diagram between the event attributes, the explicit attributes and implicit attributes related to the current emergency event can be quickly found, thereby providing a comprehensive information basis for the configuration of rescue equipment, wherein the explicit attributes can be understood as the event attributes directly related to the current emergency event, and the implicit attributes can be understood as the event attributes indirectly related to the current emergency event. For example, for a fire event, the explicit attributes include the location of the fire, the material of the fire, and the size of the fire, and the implicit attributes include surrounding important buildings, weather conditions, and the structure of the building on fire. In the above query process, for new emergency events, by splitting the new emergency events into multiple event features (event attributes), and then matching the similar features of multiple event features one by one from the emergency event knowledge graph, and then mining more related information and hidden information based on the similar features of multiple event features, it provides real and effective information basis for the configuration of rescue equipment for new emergency events; for combined events, the same processing method as new emergency events is adopted to orderly mine the potential threats and obvious threats of combined events, thereby reducing the occurrence of unreasonable resource utilization of rescue equipment.

[0040] In a possible implementation, in the process of mining the associated attributes of emergency events, it is difficult to directly obtain more comprehensive information by only inputting the event attributes into the emergency event knowledge graph once, so it is necessary to perform iterative processing to deeply mine the associated information of emergency events. Specifically: obtain the first query result of multiple event attributes in the emergency event knowledge graph, and the query result contains multiple first associated attributes; then input the first query result into the emergency event knowledge graph again to obtain the second query result, and the second query result contains multiple second associated attributes; at this time, calculate the convergence value of the second query result, if the convergence value of the second query result reaches the convergence value threshold, then stop the iteration, and use the first query result and the second query result as the associated attributes of the current emergency event, if the convergence value of the second query result does not reach the convergence value threshold, then input the second query result into the emergency event knowledge graph again, until the convergence value threshold of the query result of the current emergency event reaches the convergence value threshold, stop the iteration, and use the results of multiple iterations as the associated attributes of the current emergency event.

[0041] In a possible implementation, in order to avoid too many iterations, resulting in information overload and inaccuracy, the convergence value threshold needs to be reasonably set. Specifically: first, multiple event attributes of the emergency event are matched with the historical emergency event library to obtain multiple similar historical emergency events, and then the similarity value between the current emergency event and the multiple similar historical emergency events is calculated. If there is a similarity value greater than or equal to a preset value among the multiple similar historical emergency events, the convergence value threshold of the similar historical emergency event is selected as the convergence value threshold for the iterative processing of the current emergency event. This process matches with the historical emergency event library, and directly uses the convergence value threshold of the event with high similarity after finding it. This can avoid unnecessary multiple iterations, save a lot of computing resources and computing time, and improve the processing speed. In addition, the convergence value threshold of the known similar emergency event is used for the current emergency event to ensure the consistency and stability of the processing results, thereby improving the reliability of the final decision.

[0042] In a possible implementation, if there is no historical emergency event with a high similarity to the current emergency event in the historical emergency event database, the complexity and iteration number of the emergency event are read, and then a corresponding dynamic convergence value threshold is generated for each iteration result, so as to achieve dynamic information mining while ensuring that processing overload will not occur due to too many iterations. Specifically, the following formula can be used for calculation: Among them, T is the convergence value threshold corresponding to each iteration, is the initial default convergence value threshold, is the number of associated attributes after the nth iteration, is the number of associated attributes after the n-1th iteration, is a very small positive number, is the maximum number of iterations, and is the adjustment factor, where It is used to control the sensitivity of the convergence value threshold to the change of the number of associated attributes. Used to control how quickly the convergence threshold decreases as the number of iterations increases.

[0043] In the above formula,

[0044] It is an adjustment parameter for the convergence value threshold generated based on the change rate of the number of associated attributes (complexity change rate) during the iteration process. It can be understood that if the number of associated attributes generated by two iterations is very close, the change rate is very small, and the adjustment factor is close to 1, indicating that the convergence trend of the algorithm is not obvious at this time, and there is no need to significantly reduce the convergence threshold; if the number of associated attributes changes greatly, the adjustment factor will be less than 1. At this time, the convergence threshold is adaptively reduced to prompt the algorithm to judge whether it has converged more cautiously and avoid introducing too much interference information to affect the final result.

[0045]

[0046] In the iterative process, based on the number of iterations, an adjustment parameter for the convergence value threshold is generated. It is understandable that in the iterative process, adjusting the convergence value threshold only by relying on the change rate of the number of associated attributes is prone to enter an iterative dead loop, especially when the number of iterations is large. This phenomenon is more obvious. For example, when the number of associated attributes in each iteration is only 1, the actual change of the convergence value threshold before and after the adjustment is not obvious, and the current convergence value is far from reaching the convergence value threshold, resulting in a loop state. Therefore, by introducing the number of iterations to adjust the convergence value threshold, the iteration can be stopped when an iterative dead loop occurs. Specifically, at the beginning of the algorithm, the number of iterations is small, the adjustment parameter is close to 1, and the impact on the convergence value threshold is small; as the number of iterations increases, the adjustment parameter gradually decreases, thereby reducing the convergence value threshold, making it easier for the algorithm to converge when it is close to the maximum number of iterations.

[0047] In summary, by adjusting the convergence value threshold through the rate of change of the associated attributes at each iteration, the mining depth and breadth can be accurately grasped to avoid introducing too much interference information; in addition, in order to prevent the algorithm from entering an infinite loop, it is necessary to introduce the number of iterations to adjust the convergence value threshold, thereby limiting the mining scope of the algorithm and improving the reliability of subsequent decisions.

[0048] S104: Generate a rescue equipment table of multiple association attributes and multiple event attributes.

[0049] S105: Integrate multiple rescue equipment tables to obtain a rescue equipment configuration plan for an emergency event.

[0050] In the above steps S104-S105, the rescue equipment table contains relevant parameters and quantities of rescue equipment that process associated attributes or event attributes. For example, if the associated attribute is explosion of flammable and explosive chemicals, the rescue equipment table contains chemical protective clothing and gas detectors, etc. It can be understood that one associated attribute or event attribute can generate multiple rescue equipment tables, and multiple associated attributes or event attributes can also generate only one rescue equipment table. Therefore, it is necessary to integrate multiple rescue equipment tables in the end to determine the rescue equipment that needs to be configured and the corresponding quantity.

[0051] In a possible implementation, different rescue equipment may have the same functions, but due to changes in rescue scenarios, the rescue efficiency of different rescue equipment varies. For example, fire trucks and portable fire extinguishers can both put out fires, but fire trucks have stronger fire extinguishing capabilities, larger water volumes, and longer ranges, and are the main fire extinguishing equipment, while portable fire extinguishers are suitable for use in early fires or small-scale fires, and are secondary fire extinguishing equipment. Therefore, when integrating multiple rescue equipment tables, it is necessary to perform functional evaluations on multiple rescue equipment tables according to the current application scenarios, so as to obtain the functional adaptability of multiple rescue equipment tables, wherein the evaluation process can be obtained by calculating the functional scores of the rescue equipment, specifically: using the hierarchical analysis method to determine the weight values ​​of multiple associated attributes and event attributes, and then calculating the weighted sum score of the rescue equipment according to the performance of the rescue equipment in processing multiple associated attributes and event attributes in the actual scenario. At this time, the weighted sum score of the rescue equipment is the functional score of the rescue equipment. Finally, if the functional score of the rescue equipment is greater than or equal to the preset score, it is determined that the rescue equipment needs to be configured in the rescue equipment configuration plan.

[0052] Reference Figure 2 The present application also provides a rescue equipment configuration system for an emergency rescue vehicle, the system is an emergency rescue platform, the emergency rescue platform includes an acquisition module 1 and a processing module 2, wherein: Acquisition module 1, used to obtain basic information of emergency events; Processing module 2, used for performing keyword extraction and standardization processing on the basic information of the emergency event to obtain multiple event attributes of the emergency event; Input multiple event attributes into a preset emergency event knowledge graph to obtain multiple associated attributes; Generate a rescue equipment table with multiple associated attributes and multiple event attributes; integrate multiple rescue equipment tables to obtain a rescue equipment configuration plan for emergency events.

[0053] In a possible implementation, the basic information of the emergency event is parsed to obtain a plurality of sub-information of the emergency event and information sources corresponding to the plurality of sub-information; Classifying the multiple sub-information according to the information sources corresponding to the multiple sub-information to obtain multiple classification clusters; The keyword extraction algorithms corresponding to the multiple classification clusters are adopted to extract the keywords of the multiple classification clusters.

[0054] In a possible implementation, keyword extraction algorithms corresponding to multiple classification clusters are used to extract keywords of multiple classification clusters, which specifically includes: Acquire multiple non-keywords of a first classification cluster, where the first classification cluster is any one of the multiple classification clusters; Screening out multiple non-keywords to obtain multiple non-keywords to be verified; Perform part-of-speech tagging on multiple non-keywords to be verified; Based on the parts of speech of multiple non-keywords to be verified, the multiple non-keywords to be verified are assigned to a second classification cluster for keyword verification to extract missing keywords from the multiple non-keywords to be verified. The second classification cluster is any classification cluster among the multiple classification clusters except the first classification cluster.

[0055] In a possible implementation, a first correlation attribute between multiple event attributes is obtained; Inputting a plurality of first associated attributes into an emergency knowledge graph to generate a plurality of second associated attributes; Calculating convergence values ​​of multiple second associated attributes in the emergency event knowledge graph; If the convergence value reaches the threshold, it is determined that the plurality of first association attributes and the plurality of second association attributes are association attributes of the plurality of event attributes.

[0056] In a possible implementation, if the convergence value reaches a threshold, determining that the plurality of first association attributes and the plurality of second association attributes are association attributes of the plurality of event attributes further includes: Matching multiple event attributes of the emergency event with a historical emergency event database to obtain multiple similar historical emergency events; Calculating similarity values ​​between the emergency event and multiple similar historical emergency events; If the similarity value between the emergency event and the first similar historical emergency event is greater than or equal to a preset value, the convergence value threshold of the first similar historical emergency event is used as the threshold of the emergency event, and the first similar historical emergency event is any one of the multiple similar historical emergency events.

[0057] In a possible implementation manner, after calculating the similarity values ​​between the emergency event and a plurality of similar historical emergency events, the method further includes: If the similarity values ​​of multiple similar historical emergency events and the emergency event are all less than the preset value, the complexity and number of iterations of the emergency event are read; Based on the complexity of the emergency event and the number of iterations, the convergence value threshold of the emergency event is adjusted.

[0058] In a possible implementation, a plurality of rescue equipment tables are functionally evaluated to obtain functional adaptability of the plurality of rescue equipment tables; Based on the functional adaptability of multiple rescue equipment tables, a rescue equipment configuration plan for emergency events is generated.

[0059] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0060] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0061] The communication bus 302 is used to realize the connection and communication between these components.

[0062] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0063] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0064] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0065] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a method for configuring rescue equipment of an emergency rescue vehicle.

[0066] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a method for configuring rescue equipment of an emergency rescue vehicle stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0067] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0069] 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, that is, they 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.

[0070] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0071] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0072] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0073] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for configuring rescue equipment for an emergency rescue vehicle, characterized in that: Applied to an emergency rescue platform, the method comprises: Get basic information about emergency events; Perform keyword extraction and standardization processing on the basic information of the emergency event to obtain multiple event attributes of the emergency event; Inputting a plurality of the event attributes into a preset emergency event knowledge graph to obtain a plurality of associated attributes; generating a rescue equipment table of a plurality of the association attributes and a plurality of the event attributes; A plurality of the rescue equipment tables are integrated to obtain a rescue equipment configuration plan for the emergency event.

2. The method according to claim 1, characterized in that The extracting of keywords from the basic information of the emergency event specifically includes: Parsing the basic information of the emergency event to obtain a plurality of sub-information of the emergency event and information sources corresponding to each of the plurality of sub-information; Classifying the plurality of sub-information according to information sources corresponding to the plurality of sub-information to obtain a plurality of classification clusters; The keyword extraction algorithms corresponding to the multiple classification clusters are used to extract the keywords of the multiple classification clusters.

3. The method according to claim 2, characterized in that The step of extracting keywords of the plurality of classification clusters by using keyword extraction algorithms corresponding to the plurality of classification clusters specifically includes: Acquire multiple non-keywords of a first classification cluster, where the first classification cluster is any one of the multiple classification clusters; Screening out the plurality of non-keywords to obtain a plurality of non-keywords to be verified; Performing part-of-speech tagging on the plurality of non-keywords to be verified; Based on the parts of speech of the multiple non-keywords to be verified, the multiple non-keywords to be verified are assigned to a second classification cluster for keyword verification to extract missing keywords from the multiple non-keywords to be verified. The second classification cluster is any one of the multiple classification clusters except the first classification cluster.

4. The method according to claim 1, characterized in that The inputting of the plurality of event attributes into the preset emergency event knowledge graph to obtain a plurality of associated attributes specifically includes: Obtaining a first correlation attribute between a plurality of the event attributes; Inputting a plurality of the first associated attributes into the emergency knowledge graph to generate a plurality of second associated attributes; Calculate convergence values ​​of a plurality of the second association attributes in the emergency event knowledge graph; If the convergence value reaches a threshold, it is determined that the plurality of first association attributes and the plurality of second association attributes are association attributes of the plurality of event attributes.

5. The method according to claim 4, characterized in that If the convergence value reaches a threshold, determining that the plurality of first association attributes and the plurality of second association attributes are association attributes of the plurality of event attributes specifically includes: Matching multiple event attributes of the emergency event with a historical emergency event database to obtain multiple similar historical emergency events; Calculating similarity values ​​between the emergency event and a plurality of similar historical emergency events; If the similarity value between the emergency event and the first similar historical emergency event is greater than or equal to a preset value, the convergence value threshold of the first similar historical emergency event is used as the threshold of the emergency event, and the first similar historical emergency event is any one of the multiple similar historical emergency events.

6. The method according to claim 5, characterized in that After calculating the similarity values ​​between the emergency event and the plurality of similar historical emergency events, the method further includes: If the similarity values ​​between the plurality of similar historical emergency events and the emergency event are all less than a preset value, then reading the complexity and the number of iterations of the emergency event; Based on the complexity of the emergency event and the number of iterations, a convergence value threshold of the emergency event is adjusted.

7. The method according to claim 1, characterized in that The integration of the plurality of rescue equipment tables to obtain the rescue equipment configuration scheme for the emergency event specifically includes: Performing functional evaluation on the plurality of rescue equipment tables to obtain functional adaptability of the plurality of rescue equipment tables; Based on the functional adaptability of the plurality of rescue equipment tables, a rescue equipment configuration plan for the emergency event is generated.

8. A rescue equipment configuration system for an emergency rescue vehicle, characterized in that: The system is an emergency rescue platform, which comprises an acquisition module (1) and a processing module (2), wherein: The acquisition module (1) is used to acquire basic information of the emergency event; The processing module (2) is used to extract keywords and perform standardization processing on the basic information of the emergency event to obtain multiple event attributes of the emergency event; Inputting a plurality of the event attributes into a preset emergency event knowledge graph to obtain a plurality of associated attributes; Generate a plurality of rescue equipment tables of the associated attributes and the event attributes; integrate the plurality of rescue equipment tables to obtain a rescue equipment configuration plan for the emergency event.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

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