Emergency service resource determination method and device based on perception network model

Abstract

The application provides an emergency service resource determination method and device based on a perception network model, and relates to the technical field of emergency management.The method obtains a resource data set of a disaster environment to be measured, then pre-processes the resource data set to obtain a perception achievement data set; subsequently, the perception achievement data set is input into a trained perception network model to obtain resource demand data of the disaster environment to be measured, so that emergency service evaluation is performed through the resource demand data.Based on this, the application can accurately analyze a disaster situation, intelligently analyze and accurately estimate required emergency resources, thereby overcoming problems such as information fragmentation, uneven resource allocation and low matching efficiency in the management and deployment of current emergency service resources, and improving the accuracy of disaster site situation measurement.

Description

Technical Field

[0001] The present invention relates to the field of emergency management technology, and in particular to a method and device for determining emergency service resources based on a perception network model. Background Art

[0002] Typically, responding to emergencies such as natural disasters and accidents requires emergency management personnel to quickly and accurately deploy emergency service resources. However, existing technologies primarily rely on manual statistics and traditional paper-based records for emergency service resource management. This approach suffers from issues such as delayed information updates and difficulty sharing data. This makes it difficult for emergency management personnel to quickly obtain comprehensive and accurate resource information when faced with emergencies, hindering the speed and effectiveness of emergency responses.

[0003] Furthermore, traditional resource allocation methods are based on empirical judgment and lack scientific and systematic analytical tools, which can lead to imbalanced resource allocation. Furthermore, there is considerable uncertainty in the perception of disaster-site situations and the assessment of rescue capacity needs, further exacerbating the difficulty of resource allocation.

[0004] Based on this, there is an urgent need for an emergency management system that can dynamically monitor resource status in real time, quickly summarize resource needs, accurately assess the situation at the disaster site, and scientifically predict rescue capacity needs. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an emergency service resource determination method and equipment based on a perception network model, which can accurately analyze and evaluate the disaster situation, and intelligently analyze and accurately estimate the required emergency resources, overcome the current management and allocation of emergency service resources, such as information fragmentation, uneven resource distribution, and low matching efficiency, and improve the accuracy of disaster site situation measurement.

[0006] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0007] In a first aspect, the present invention provides a method for determining emergency service resources based on a perception network model, comprising the following steps:

[0008] Obtaining a resource data set of the disaster environment to be tested; wherein the resource data set includes multiple types of disaster characteristic data;

[0009] Preprocess the resource dataset to obtain the perception result dataset;

[0010] Input the perception result data set into the trained perception network model to obtain the resource demand data of the current disaster environment to be tested, so as to evaluate the emergency service through the resource demand data;

[0011] The resource demand data includes multiple resource demand categories and the quantity of resources matching each resource demand category.

[0012] Optionally, the step of preprocessing the resource dataset to obtain the perception result data includes:

[0013] Eliminate noise in each disaster characteristic data under the resource dataset;

[0014] Normalizing the characteristic data of each disaster after noise elimination to obtain normalized characteristic data of each disaster;

[0015] The target features of the normalized disaster characteristic data are extracted and labeled to obtain the feature labels related to the disaster environment to be measured and the perception result data set.

[0016] Optionally, the step of inputting the perception result data set into the trained perception network model to obtain resource demand data of the current disaster environment to be tested includes:

[0017] Matching resources corresponding to each target feature under the current disaster type based on the knowledge graph matching perception results dataset;

[0018] Determine the similarity score between each target feature and the corresponding matching resource;

[0019] The number of emergency resources corresponding to each target feature is calculated based on the similarity score.

[0020] Optionally, the step of matching resources corresponding to each target feature in the perception achievement dataset under the current disaster type based on the knowledge graph matching includes:

[0021] Based on the knowledge graph matching perception results dataset, the initial matching resource category corresponding to each target feature under the current disaster type;

[0022] Calculate the matching degree between each target feature and the corresponding initial matching resource category;

[0023] According to the matching degree, the matching resource categories that meet the preset matching value are selected from the corresponding initial matching resource categories as the final matching resources.

[0024] Optionally, the calculation formula of the matching degree satisfies:

[0025]

[0026] Among them, MD is the matching degree, n is the total number of target features in the perception result dataset, and w j is the weight of the jth target feature, x j is the value of the jth target feature in the perception result dataset, y jis the reference value of the jth target feature in the domain knowledge graph, sin(x j ,y j ) is the similarity function value of the j-th target feature.

[0027] Optionally, the step of determining a similarity score between each target feature and the corresponding resource requirement category includes:

[0028] Calculate the similarity between each target feature and the corresponding matching resource;

[0029] The similarity scores between the corresponding matching resources are determined based on the similarity.

[0030] Optionally, the step of calculating the number of emergency resources corresponding to each target feature according to the similarity score includes:

[0031] Determine the initial number of emergency resources based on the similarity score and the preset number of resources;

[0032] Determine the dynamic adjustment coefficient based on the dynamic parameters of the perception results data set;

[0033] The number of emergency resources corresponding to each target characteristic is determined based on the initial number of emergency resources and the dynamic adjustment coefficient.

[0034] Optionally, the formula for determining the number of emergency resources satisfies:

[0035]

[0036] Where Q is the amount of emergency resources; s i Score the similarity between the i-th target feature and the corresponding matching resource category; d i is the preset resource quantity corresponding to the i-th target feature; c m is the meteorological condition adjustment coefficient; c i is the illumination adjustment coefficient; c t is the traffic condition adjustment coefficient.

[0037] Optionally, the step of inputting the perception result data set into the trained perception network model to obtain resource demand data of the current disaster environment to be tested further includes:

[0038] Determine whether the difference between the quantity of each emergency resource and the corresponding preset resource quantity is equal to the threshold range; if so, use the emergency resource quantity as the emergency resource quantity corresponding to the current target feature, obtain the emergency resource quantity corresponding to multiple target features and each target feature, and determine the resource demand data of the current disaster environment to be tested;

[0039] And update the corresponding preset resource quantity based on the emergency resource quantity corresponding to the current target characteristics.

[0040] In a second aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement the emergency service resource determination method described in any one of the first aspects above.

[0041] An embodiment of the present invention provides a method and device for determining emergency service resources based on a perception network model. The determination method obtains a resource data set of the disaster environment to be tested, and then pre-processes the resource data set to obtain a perception result data set; the perception result data set is then input into a trained perception network model to obtain resource demand data of the current disaster environment to be tested, so as to perform emergency service evaluation based on the resource demand data; wherein the resource demand data includes multiple resource demand categories and the number of resources that match each of the resource demand categories. Based on this, the present invention can accurately analyze and evaluate the disaster situation, and intelligently analyze and accurately estimate the required emergency resources, thereby overcoming the problems of information fragmentation, uneven resource distribution, and low matching efficiency in the current management and deployment of emergency service resources, and improving the accuracy of disaster site situation measurement.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 A flowchart showing the steps of a method for determining emergency service resources provided by an embodiment of the present invention is shown;

[0045] Figure 2 A step-by-step flow chart of step 100 in an embodiment of the present invention is shown;

[0046] Figure 3 A step-by-step flow chart of step 200 in an embodiment of the present invention is shown;

[0047] Figure 4 FIG2 shows a process flow chart of step 200 in an embodiment of the present invention;

[0048] Figure 5 FIG. 1 shows one of the step-by-step flow charts of step 300 in an embodiment of the present invention;

[0049] Figure 6A step-by-step flow chart of step 301 in an embodiment of the present invention is shown;

[0050] Figure 7 A step-by-step flow chart of step 302 in an embodiment of the present invention is shown;

[0051] Figure 8 FIG3 shows a processing flow chart of step 303 in an embodiment of the present invention;

[0052] Figure 9 FIG2 shows a second step-by-step flow chart of step 300 in an embodiment of the present invention;

[0053] Figure 10 A schematic diagram of the structure of a server in an embodiment of the present invention is shown.

[0054] Icon: 10-Server. DETAILED DESCRIPTION

[0055] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0056] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely intended to represent selected embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0057] It should be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not preclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0058] As described in the background technology, the current management and deployment of emergency service resources have problems such as information fragmentation, uneven resource distribution, and low matching efficiency.

[0059] Based on the above problems, this application provides a method that can accurately analyze and evaluate the disaster situation, and intelligently analyze and accurately estimate the required emergency resources, providing intelligent services for rapid decision-making and scheduling of emergency service resources.

[0060] The following describes in detail the method and device for determining emergency service resources based on the perception network model provided by this application.

[0061] Please refer to Figure 1 , Figure 1 A flowchart of the steps of a method for determining emergency service resources based on a perception network model provided by the present invention is shown. The method for determining emergency service resources in this application includes steps 100 to 300.

[0062] Step 100: Obtain a resource dataset of the disaster environment to be tested.

[0063] Among them, the resource dataset includes various types of disaster characteristic data.

[0064] Step 200: pre-process the resource dataset to obtain a perception result dataset;

[0065] Step 300: Input the perception result data set into the trained perception network model to obtain the resource demand data of the current disaster environment to be tested, so as to conduct emergency service evaluation based on the resource demand data.

[0066] The resource demand data includes multiple resource demand categories and the quantity of resources matching each resource demand category.

[0067] Based on the perception network theory, the present invention constructs an emergency service resource determination method based on the perception network model. The method uses the perception modeling theory to evaluate the capabilities and analyze the modeling of emergency service resources. On the basis of comprehensive acquisition of various data and information at the incident site and situation analysis, the method can uniformly manage various resource demands at the incident site, quantify the specific demands, and then obtain the resource demand data of the current disaster environment to be tested, so that emergency service management personnel can quickly determine the decision of emergency service resources based on the resource demand data to ensure the safety of personnel.

[0068] It should be noted that the resource demand data in this embodiment includes multiple resource demand categories and the quantity of resources matching each resource demand category. The above-mentioned resource demand categories can be the type (or attribute) of rescue teams and rescue supplies, and the quantity of resources is the number of rescue teams under the corresponding rescue team type, the corresponding quantity of various rescue supplies, and so on.

[0069] To ensure the diversity of data indicators and improve the accuracy of the emergency service resource determination method in this application, please Figure 1 Based on the reference Figure 2 , Figure 2 A step-by-step flow chart of step 100 in this embodiment is shown, wherein step 100 in this embodiment includes step 101 and step 102.

[0070] Step 101: Build a disaster sensing network.

[0071] Step 102: Acquire real-time data of the disaster environment to be measured and data parameters related to the current disaster environment to be measured through the disaster sensing network to obtain a resource data set.

[0072] The resource data set includes at least environmental monitoring data, personnel distribution data and traffic condition data.

[0073] In this embodiment, a specific implementation method for obtaining a resource data set of a disaster environment to be detected can rely on a disaster sensing network to collect data from the scene of a sudden incident in real time.

[0074] In one possible implementation, the disaster awareness network described above could be constructed by deploying various physical sensors in key urban areas, such as commercial districts, residential areas, and major transportation routes. These sensors would monitor indoor and outdoor environmental parameters in real time, enabling real-time data corresponding to the specific disaster environment to be detected when a disaster strikes. Furthermore, the disaster awareness network could integrate and develop data interface programs to connect to mainstream social media platforms and obtain public information related to disasters, including text, images, and videos.

[0075] In this embodiment, the disaster sensing network can also integrate the aforementioned multi-type, multi-source data, using the integrated dataset as the final resource dataset. In one possible implementation, this resource dataset includes at least key information such as the address of the disaster environment to be detected, the time of the disaster, the scope of impact, and the disaster event. The multi-type disaster characteristic data described in this application is the aforementioned key information related to the different disaster environments to be detected.

[0076] In addition, this application also provides an implementation method for obtaining a resource data set of the disaster environment to be tested. It should be noted that this method can supplement the implementation method of the previous embodiment to improve the richness of disaster feature data and improve the accuracy of the emergency service resource determination method.

[0077] The supplementary implementation methods described in this embodiment may include: drone inspections and manual information collection. Drone inspections involve equipping drones with high-precision radiation detectors, high-definition cameras, and infrared thermal imagers. After a disaster occurs, the modified drones are used to monitor the radiation dose rate in the air in real time. High-definition cameras and infrared thermal imagers are used to capture on-site images and videos to identify the location of the disaster source, the direction and extent of the disaster spread, and the conditions of affected buildings and trapped personnel. Manual information collection involves rescue personnel manually collecting information at the disaster site, recording the needs of the affected population, and counting the amount of damaged infrastructure.

[0078] Based on the above implementation method, after obtaining the resource data set, the various types of disaster characteristic data can be further pre-processed. Figure 1 Based on the reference Figure 3 , Figure 3 A step-by-step flow chart of step 200 is shown, which includes steps 201 to 203 .

[0079] Step 201: Eliminate noise from each disaster characteristic data in the resource dataset.

[0080] Step 202: normalize the disaster characteristic data after noise elimination to obtain normalized disaster characteristic data.

[0081] Step 203: extract target features of each normalized disaster feature data, and label the target features to obtain feature labels related to each disaster feature data and the disaster environment to be measured as a perception result dataset.

[0082] In this embodiment, before extracting the target features of each disaster characteristic data, it is necessary to first perform data cleaning and format standardization operation procedures on the resource data set to improve the accuracy of the data.

[0083] In one possible implementation, the noise of each disaster characteristic data under the resource data set is eliminated in step 201, and the data cleaning is implemented as follows: using statistical analysis methods to identify and eliminate abnormal data points caused by equipment failure or signal interference, and obtain each disaster characteristic data excluding abnormal values; then using interpolation, regression analysis and other technologies to reasonably fill in the missing data points to ensure the integrity of the data; after grouping, the data records can be compared to identify and delete duplicate data entries, eliminate redundant parameters, and thus obtain each disaster characteristic data after noise elimination.

[0084] It should be noted that this application does not limit the order of operations or specific implementation methods of the above data cleaning, as long as the integrity and accuracy of the data can be guaranteed. The above implementation method is only a simple exemplary description and should not be regarded as a limitation on the data cleaning steps.

[0085] In one possible implementation, the normalization operation in step 202 is to uniformly convert data from different channels and formats into a structured format acceptable to the analysis model to obtain normalized characteristic data of each disaster.

[0086] In order to improve the convenience of the emergency service resource determination method, after normalizing the resource data set, target features can be extracted from different types of disaster characteristic data under the resource data set, and the extracted target features can be marked to further determine the precise location and impact range of the disaster and the time of the disaster, so as to facilitate the subsequent use of the perception network model to determine the resource demand data.

[0087] In this embodiment, step 203 may be implemented as follows:

[0088] Feature extraction is performed on different types of data in the normalized disaster feature data.

[0089] Taking sound data processing as an example, we can first apply speech recognition technology to convert the sound signal into text data, and then use natural language processing technology to extract key words describing the nature and urgency of the disaster from the text data.

[0090] Taking video data processing as an example, if dynamic image processing is adopted, motion detection and behavior recognition technology can be used to extract dynamic information in the video, such as crowd flow and vehicle movement, and then evaluate the impact of the disaster on traffic and personnel distribution; if static image processing is adopted, video frame extraction technology is used to capture static images of the disaster scene to further analyze the impact of the disaster on buildings and other infrastructure.

[0091] Taking text data processing as an example, keyword extraction and context recognition can be performed directly. For example, natural language processing technology can be used to extract keywords from text and identify context to understand the descriptive information of the disaster scene.

[0092] Furthermore, after obtaining the characteristics corresponding to the above different data samples, the integrated data can be classified according to the characteristics and usage of the data. In this embodiment, the data types are divided into: environmental monitoring, including the distribution range, density, and degree of harmful radiation and harmful gases; personnel distribution, including population density and personnel flow trajectory; traffic conditions, such as urban road density, road traffic volume, road congestion, and road peak traffic time.

[0093] Finally, to ensure data consistency and comparability, the extracted features can be normalized and described using the Sensor-ML language. Based on this approach, the present invention can aggregate and numerically describe resource data of different categories, enabling quantitative management of various sensing resources in disaster environments and obtaining a final sensing dataset.

[0094] Among them, you can refer to Figure 4 , Figure 4The processing flow chart of step 200 in this embodiment is shown. The perception result data set may include data such as time, wind force, wind direction, disaster type, disaster level, crowd, disaster situation, roads, facilities, etc.

[0095] To facilitate reference by emergency management service personnel, this application also discloses a data preprocessing method. After obtaining the final perception result data set, data fusion technologies such as data association and data fusion algorithms can be used to integrate relevant information scattered in different data sources to form a complete disaster event data view.

[0096] Before using the trained perception network model to obtain the resource demand data of the current disaster environment to be tested, this application can also first filter the data under the perception results data set to obtain target features that are more in line with the evaluation indicators.

[0097] In this embodiment, a performance evaluation model may be constructed first. The performance evaluation model may use linear regression analysis to determine the linear relationship between target features. The calculation formula may be expressed as:

[0098] Y=Xβ+ε;

[0099] Where Y is the dependent variable, which represents the evaluation result of emergency service effectiveness; X is the independent variable matrix, which represents the target characteristics of disaster data; β is the regression coefficient vector, which represents the influence of each characteristic on effectiveness evaluation; ε is the error term, which represents the random error of the model.

[0100] Among them, the regression coefficient β can be estimated by the least squares method to minimize the residual sum of squares, that is, the calculation formula of the regression coefficient vector can be expressed as: β^=argmin β ||Y-Xβ|| 2 , where argmin β ||.|| 2 is the calculation function of the least squares method.

[0101] Furthermore, by comparing the evaluation results corresponding to each target feature, the target features with better evaluation results can be screened out from the target features, for example, the target features with evaluation values ​​lower than the preset values ​​can be eliminated, and then the disaster feature data corresponding to the final target feature can be obtained to form the perception results data set.

[0102] Based on this, this application can further screen target characteristics that are more in line with the evaluation indicators through the evaluation results of emergency service effectiveness.

[0103] Please Figure 1 Based on the reference Figure 5 , Figure 5 A step-by-step flow chart of step 300 is shown, which includes steps 301 to 303 .

[0104] Step 301: Match the corresponding matching resources of each target feature in the perception result dataset under the current disaster type based on the knowledge graph.

[0105] Step 302: Determine a similarity score between each target feature and the corresponding matching resource.

[0106] Step 303: Calculate the number of emergency resources corresponding to each target feature based on the similarity score.

[0107] In this embodiment, the construction and training of the perception network model can involve training a radioactive accident emergency service effectiveness assessment model based on historical disaster data and a machine learning algorithm. This perception network model can assess the type and quantity of emergency resources required based on factors such as the disaster's scale, intensity, and impact range. In one possible implementation, the machine learning algorithm may include a decision tree algorithm and a knowledge graph model. Based on these, the perception network model can predict the effectiveness of resources such as vehicles, equipment, and rescue personnel based on characteristics such as the disaster type, affected area, spread rate, and population density in the affected area. Simultaneously, the knowledge graph model is used to match resource requirement categories with corresponding resource quantities. For example, based on the radiation type, coverage area, and radiation spread rate, a corresponding rescue plan can be intelligently matched, including the required resource requirement categories and corresponding resource quantities.

[0108] In this embodiment, a domain knowledge graph can be constructed by collecting and organizing professional knowledge in the field of emergency rescue, including disaster types, emergency resources, rescue processes, disposal methods, etc.

[0109] After obtaining the perception network model, the resource demand data of the current disaster environment to be tested can be obtained using the perception network model. Figure 5 Based on the reference Figure 6 , Figure 6 A step-by-step flowchart of step 301 is shown, where step 301 includes steps 3011 to 3013 .

[0110] Step 3011: Initially match resource categories corresponding to each target feature in the perception result dataset under the current disaster type based on the knowledge graph matching.

[0111] Step 3012: Calculate the matching degree between each target feature and the corresponding initial matching resource category.

[0112] Step 3013: Filter matching resource categories that meet the preset matching value from the corresponding initial matching resource categories based on the matching degree, and use them as final matching resources.

[0113] In this embodiment, the calculation formula of the matching degree in step 3012 satisfies:

[0114]

[0115] Among them, MD is the matching degree, n is the total number of target features in the perception result dataset, and w j is the weight of the jth target feature, x j is the value of the jth target feature in the perception result dataset, y j is the reference value of the jth target feature in the knowledge graph, sin(x j ,y j ) is the similarity function value of the j-th target feature.

[0116] The role of the knowledge graph model in this embodiment is to obtain disaster characteristic information, such as the above-mentioned time, wind speed, wind force and other information, and then search in the knowledge graph through semantic understanding to obtain the corresponding entities and relationships, that is, the resource requirements such as rescue teams, rescue capabilities, equipment types, etc. corresponding to the time, wind speed, wind force and other information, and obtain the initial matching resource categories.

[0117] It should be noted that the data information in the initial matching resource category comes from the above-mentioned professional knowledge collected and organized in the field of emergency rescue.

[0118] In this embodiment, a graph traversal algorithm is also used to traverse the path information corresponding to the category information in the initial matching resource category in the knowledge graph, and a similarity evaluation is performed on the path information, that is, the matching degree between each target feature and the corresponding initial matching resource category is calculated in step 3012, so as to match and infer the types of rescue supplies required for each category for different perception elements (labels corresponding to the above target features).

[0119] Furthermore, this embodiment can screen the final matching resources that match any perception element through a preset matching degree value. For example, when the matching degree MD is greater than the preset matching value T, it is considered that the current perception element matches the reference value in the knowledge graph, and the required type of rescue supplies is obtained.

[0120] After obtaining the final matched resources after screening, the similarity score between each target feature and the corresponding matched resource is determined, and then the number of emergency resources corresponding to each target feature is obtained using the similarity score.

[0121] Please Figure 5 Based on the reference Figure 7 , Figure 7 A step-by-step flow chart of step 302 is shown. In this embodiment, step 302 includes steps 3021 and 3022.

[0122] Step 3021: Calculate the similarity between each target feature and the corresponding matching resource.

[0123] Step 3022: Determine the similarity scores between the corresponding matching resources based on the similarity.

[0124] It should be noted that this embodiment does not limit the calculation method of the similarity. In one possible implementation, the calculation formula of the similarity S can be expressed as:

[0125]

[0126] In the display, w i is the weight of the i-th target feature, m i is the matching degree of the i-th target feature, and its value is 0 or 1.

[0127] Then, based on the similarity value, a matching score can be generated for the matching resource corresponding to each target feature, and the matching score range is [0, 1].

[0128] Based on this, please Figure 5 Based on the reference Figure 8 , Figure 8 A step-by-step flow chart of step 303 is shown. In this embodiment, step 303 includes steps 3031 to 3033.

[0129] Step 3031: Determine the initial emergency resource quantity based on the similarity score and the preset resource quantity.

[0130] Step 3032: Determine a dynamic adjustment coefficient based on the dynamic parameters of the perception result dataset.

[0131] Step 3033: Determine the number of emergency resources corresponding to each target feature based on the initial number of emergency resources and the dynamic adjustment coefficient.

[0132] In this embodiment, the formula for determining the number of emergency resources satisfies:

[0133]

[0134] Where Q is the amount of emergency resources; s i Score the similarity between the i-th target feature and the corresponding matching resource category; d i is the preset resource quantity corresponding to the i-th target feature; c m is the meteorological condition adjustment coefficient; c i is the illumination adjustment coefficient; c t is the traffic condition adjustment coefficient.

[0135] In this embodiment, the initial number of emergency resources can be determined based on the similarity score and the preset number of resources through linear interpolation or weighted average calculation. The calculation method of the initial number of emergency resources can be expressed as: In the figure, Q is the amount of emergency resources; si Score the similarity between the i-th target feature and the corresponding matching resource category; d i is the preset resource quantity corresponding to the i-th target feature.

[0136] In order to further simulate the possible paths of disaster development and predict secondary disasters and long-term impacts, the perception network model in this application also includes a time series analysis model or a numerical simulation model, and then the development trend of the disaster is predicted through the perception network model to obtain the disaster status at future moments, including possible paths, changes in the scope of impact, and the probability of secondary disasters.

[0137] In one possible implementation, the perception network model in this application can monitor and predict dynamic factors such as weather changes, lighting environment changes, and traffic environment changes in real time, and determine a dynamic adjustment coefficient based on the dynamic factors to optimize the initial number of emergency resources.

[0138] When the dynamic adjustment coefficient includes the meteorological condition adjustment coefficient c m , Lighting Condition Adjustment Coefficient c l and traffic condition adjustment coefficient c t In this embodiment, the formula for determining the number of emergency resources satisfies:

[0139]

[0140] It should be noted that this embodiment does not limit the process of determining the dynamic adjustment coefficient of the time series analysis model based on dynamic factors.

[0141] Furthermore, this application can also use the analytical and deductive capabilities of the knowledge graph in the field of disaster chains to interpret and identify the future environmental situation in the disaster-affected area. Based on the dynamic changes in various target characteristics, such as environmental changes, the occurrence of potential secondary disasters, and the status of environmental data, the risk factors that affect rescue can be interpreted and identified, and then the new resource requirements that may occur can be calculated. The calculation formula for the new resource requirements can satisfy: RD = f(E, SD, PD);

[0142] In the figure, RD represents the additional resource demand, E represents the environmental change parameter, SD represents the probability of secondary disasters, PD represents the main risk factor parameter, and f is a function used to calculate resource demand based on these parameters.

[0143] Furthermore, this application can further perform calculations based on the above resource demand data in combination with specific disaster parameters to determine the specific quantity requirements for each resource. For example, the required quantity of protective clothing, decontamination equipment, medical supplies and other resources can be calculated based on factors such as radiation levels, affected areas, and population density. The specific quantity requirements are presented in the form of data values, which directly reflect the required quantity of resources, making it easier for decision makers to allocate resources and make decisions.

[0144] In one possible implementation method, the calculation process of the above-mentioned specific resource demand values ​​can be understood as follows: the required number of rescue personnel P can be determined based on the disaster-stricken area A (unit: square kilometers) and the per capita number of rescue personnel r (unit: person / square kilometer), and the calculation formula for the required number of rescue personnel P satisfies: P=A×r; after the required number of rescue personnel P is determined, the required number of rescue equipment D is calculated based on the number of equipment required for each rescuer e (unit: equipment / person), and the calculation formula satisfies: D=P×e; in addition, the total amount of materials required M can also be calculated based on the disaster-stricken population N (unit: person) and the per capita material demand s (unit: unit material / person), and the calculation formula satisfies: M=N×s.

[0145] This application can obtain the final resource demand data based on the above two determination methods and generate a resource demand list. The list details the size of the rescue team, the level of rescue capabilities, the type of equipment, and the type and quantity of rescue supplies, providing clear guidance for resource allocation.

[0146] In summary, the present invention can achieve accurate estimation of emergency service capabilities by integrating multiple data sources and intelligent analysis technologies, improve estimation accuracy, and provide a scientific basis for emergency decision-making.

[0147] To further improve the sustainability of the perception network model in this application, please Figure 5 Based on the reference Figure 9 , Figure 9 A step-by-step flowchart of step 300 is also shown, wherein step 300 further includes step 304 .

[0148] Step 304: Determine whether the difference between the quantity of each emergency resource and the corresponding preset resource quantity is equal to a threshold range;

[0149] If they are equal, the emergency resource quantity is used as the emergency resource quantity corresponding to the current target feature, and the emergency resource quantity corresponding to the multiple target features and each target feature is obtained to determine the resource demand data of the current disaster environment to be tested; and the process returns to step 303, and the corresponding preset resource quantity is updated according to the emergency resource quantity corresponding to the current target feature;

[0150] Otherwise, the corresponding preset resource quantity is used as the resource demand data of the current disaster environment to be tested.

[0151] In this embodiment, the quantity range corresponding to the preset resource types in the existing resource demand template can be matched with the characteristics of the current disaster environment to be tested. When the actual demand value (the number of emergency resources corresponding to the current target characteristics) falls within the quantity range preset in the resource demand template, the match is considered successful. The existing resource demand template is constructed by presetting the impact range, affected groups, impact time, secondary disaster possibility, and professional requirements for emergency services for different disaster levels.

[0152] At the same time, the emergency resource quantity corresponding to the current target feature is used to update the corresponding preset resource quantity, so as to improve the effectiveness of the emergency service resource determination method in the present invention.

[0153] Based on this, emergency management personnel can make targeted resource allocation suggestions based on the above resource demand data to improve the efficiency of emergency resource utilization, and adjust resource allocation plans with real-time feedback to improve the response speed and handling capabilities of emergency services.

[0154] Similar to the idea of ​​the previous embodiment, the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement the steps of the above-mentioned emergency service resource determination method.

[0155] Similar to the previous embodiment, please refer to Figure 10 , Figure 10 A block diagram of a server is provided. The server 10 includes a memory, a processor, and a communication module. The memory, processor, and communication module components are electrically connected to each other, directly or indirectly, to enable data transmission or exchange. For example, these components may be electrically connected to each other via one or more communication buses or signal lines.

[0156] The memory is used to store programs or data. The memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0157] The processor is used to read / write data or programs stored in the memory and execute corresponding functions.

[0158] The communication module is used to establish a communication connection between the server and other communication terminals through the network, and to send and receive data through the network.

[0159] It should be understood that Figure 10 The structure shown is only a schematic diagram of the server structure, and the server may also include Figure 10 More or fewer components than shown, or with Figure 10 Different configurations shown. Figure 10 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a portion of code, and the module, program segment or a portion of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0161] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0162] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0163] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for determining emergency service resources based on a perception network model, characterized in that: The following steps are involved: Acquire a resource data set of the disaster environment to be tested; wherein the resource data set includes multiple types of disaster characteristic data; Preprocessing the resource dataset to obtain a perception result dataset; Inputting the perception result data set into the trained perception network model to obtain resource demand data of the current disaster environment to be tested, so as to perform emergency service evaluation based on the resource demand data; The resource demand data includes a plurality of resource demand categories and the quantity of resources matching each resource demand category; The step of obtaining resource demand data of the current disaster environment to be tested includes: Matching the matching resources corresponding to each target feature in the perception achievement dataset under the current disaster type based on the knowledge graph; Determine the similarity score between each target feature and the corresponding matching resource; Determining an initial emergency resource quantity based on the similarity score and the preset resource quantity; Determining a dynamic adjustment coefficient based on the dynamic parameters of the perception result data set; Determining the amount of emergency resources corresponding to each target feature based on the initial amount of emergency resources and the dynamic adjustment coefficient; The formula for determining the number of emergency resources satisfies: ； Where, The number of emergency resources; For the i Similarity score between target features and corresponding matching resource categories; For the i The preset number of resources corresponding to each target feature; is the meteorological condition adjustment factor; is the light condition adjustment factor; is the traffic condition adjustment coefficient.

2. The method for determining emergency service resources according to claim 1, wherein: The steps of preprocessing the resource data set to obtain the perception result data include: Eliminating noise in each of the disaster characteristic data under the resource dataset; Normalizing the disaster characteristic data after noise removal to obtain normalized disaster characteristic data; The target features of each of the normalized disaster feature data are extracted and labeled to obtain feature labels related to the disaster environment to be measured for each of the disaster feature data, thereby obtaining a perception result data set.

3. The method for determining emergency service resources according to claim 1, wherein: The step of matching resources corresponding to each target feature in the perception result dataset under the current disaster type based on the knowledge graph includes: Matching the initial matching resource categories corresponding to each target feature in the perception achievement dataset under the current disaster type based on the knowledge graph; Calculate the matching degree between each target feature and the corresponding initial matching resource category; According to the matching degree, matching resource categories that meet the preset matching value are selected from the corresponding initial matching resource categories as final matching resources.

4. The method for determining emergency service resources according to claim 3, wherein: The calculation formula of the matching degree satisfies: ； Where MD is the matching degree, n is the total number of target features in the perception results dataset, w j It is j The weight of the target feature, x j It is the first j The value of the target feature, It is the first j The reference value of the target feature, It is j The similarity function value of the target features.

5. The method for determining emergency service resources according to claim 1, wherein: The step of determining the similarity score between each target feature and the corresponding resource demand category includes: Calculate the similarity between each target feature and the corresponding matching resource; A similarity score between corresponding matching resources is determined based on the similarity.

6. The method for determining emergency service resources according to claim 1, wherein: The step of inputting the perception result data set into the trained perception network model to obtain resource demand data of the current disaster environment to be tested also includes: Determine whether the difference between each of the emergency resource quantities and the corresponding preset resource quantity is equal to a threshold range; if so, use the emergency resource quantity as the emergency resource quantity corresponding to the current target feature, obtain the emergency resource quantity corresponding to the multiple target features and each target feature, and determine the resource demand data of the current disaster environment to be tested; And update the corresponding preset resource quantity based on the emergency resource quantity corresponding to the current target characteristics.

7. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement the emergency service resource determination method according to any one of claims 1 to 6.

Images