A material demand forecasting method and system

Abstract

The present application provides a material demand forecasting method and system, applicable to the field of data processing technology, the method comprising: obtaining earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, and earthquake scene emergency service information; randomly generating multiple initial prediction parameter information; screening the initial prediction parameter information to determine target prediction parameter information; calculating the number of casualties based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable; calculating the material demand based on the earthquake scene emergency service information and the number of casualties information. The present application combines earthquake intensity characterization information with emergency disaster relief information in the earthquake scene to quickly forecast the number of casualties after a disaster, and then forecasts and calculates the material demand quantity, thereby improving the timeliness and effectiveness of emergency rescue after an earthquake disaster.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and in particular to a material demand forecasting method and system. Background Art

[0002] Emergency relief supplies are the material guarantee and basis for implementing emergency rescue. Material demand forecasting is an indispensable part of disaster management. Effective forecasting can more reasonably allocate rescue resources, reduce unnecessary material reserves and transportation costs, save economic resources, and at the same time improve response speed and reduce casualties caused by disasters. It is of great significance to reducing the negative impact of disasters and protecting people’s lives and property.

[0003] Existing technologies typically use empirical methods such as fuzzy comprehensive evaluation, the Analytic Hierarchy Process (AHP), and domino effect analysis to directly predict emergency relief supply needs. However, these methods often lack accuracy due to incomplete consideration of influencing factors and the difficulty in collecting effective case studies for responding to sudden, multi-factor, and multi-indicator major earthquake disasters. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a material demand forecasting method and system, which aims to solve the problem in the existing technology that it is impossible to effectively analyze the multiple factors and multiple indicators of major earthquake disasters, resulting in low accuracy and effectiveness in forecasting the demand for rescue materials.

[0005] A first aspect of an embodiment of the present application provides a material demand forecasting method, comprising:

[0006] Obtain earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, and earthquake scene emergency service information;

[0007] Randomly generate multiple initial prediction parameter information;

[0008] Screening the initial prediction parameter information to determine target prediction parameter information;

[0009] Calculating the number of casualties based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable;

[0010] The material demand is calculated based on the earthquake scene emergency service information and the number of casualties.

[0011] A second aspect of an embodiment of the present application provides a material demand forecasting system, including:

[0012] An information acquisition module is used to obtain earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, and earthquake scene emergency service information;

[0013] An initial prediction parameter information generation module is used to randomly generate multiple initial prediction parameter information;

[0014] a target prediction parameter information determination module, configured to screen the initial prediction parameter information and determine target prediction parameter information;

[0015] a casualty information calculation module, configured to calculate casualty information based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable; and

[0016] The material demand calculation module is used to calculate the material demand based on the earthquake scene emergency service information and the number of casualties information.

[0017] A third aspect of an embodiment of the present application provides a terminal device, which includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the material demand forecasting method described in the first aspect above.

[0018] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, comprising: storing a computer program, which, when executed by a processor, implements the steps of the material demand forecasting method described in the first aspect above.

[0019] Compared with the prior art, the embodiments of the present application have the following beneficial effects: by analyzing and processing multiple influencing factors of earthquake disasters, the number of casualties is predicted and calculated, and based on the predicted number of casualties, the demand for rescue materials required after the disaster is predicted and calculated, and by screening parameters in the prediction and calculation process, the effective parameters after screening are combined with a preset nonlinear feature mapping function and the displacement variables of the features in the nonlinear space, the nonlinear features in the earthquake influencing factors are fully extracted and analyzed, thereby improving the accuracy and effectiveness of the post-disaster material demand prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 This is a schematic diagram of the implementation process of the material demand forecasting method provided in Example 1 of the present application;

[0022] Figure 2 This is a schematic diagram of the implementation flow of the material demand forecasting method provided in Example 2 of the present application;

[0023] Figure 3 This is a schematic diagram of the implementation flow of the material demand forecasting method provided in Example 3 of the present application;

[0024] Figure 4 This is a schematic diagram of the implementation flow of the material demand forecasting method provided in Example 4 of the present application;

[0025] Figure 5 This is a schematic diagram of the implementation flow of the material demand forecasting method provided in Example 5 of the present application;

[0026] Figure 6 This is a schematic diagram of the implementation flow of the material demand forecasting method provided in Example 6 of the present application;

[0027] Figure 7 This is a schematic diagram of the structure of the material demand forecasting system provided in an embodiment of the present application;

[0028] Figure 8 It is a schematic diagram of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, systems, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0030] In order to illustrate the technical solution described in this application, specific embodiments are provided below.

[0031] Figure 1 The following is a flowchart of the material demand forecasting method provided in Example 1 of the present application, which is described in detail as follows:

[0032] Step S101, obtaining earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, and earthquake scene emergency service information.

[0033] In this embodiment, earthquake intensity characterization information and earthquake occurrence time can be obtained by measuring and analyzing various sensors installed in the earthquake area, and earthquake scene information and earthquake scene emergency service information can be obtained from social survey reports at the location where the earthquake occurred or data recorded by the emergency command center.

[0034] In this embodiment, preferably, the earthquake intensity characterization information includes earthquake magnitude information and earthquake intensity information; the earthquake scene information includes seismic fortification intensity information, housing damage degree characterization information, forecast level characterization information, disaster-affected population information and population density information.

[0035] Step S102: randomly generate a plurality of initial prediction parameter information.

[0036] In this embodiment, the initial prediction parameter information is randomly generated. It can be understood that a large amount of initial prediction parameter information is randomly generated for subsequent screening, and the prediction parameter information with the best prediction calculation effect is screened out as the target prediction parameter information for predicting the number of deaths and casualties.

[0037] Step S103: Screen the initial prediction parameter information to determine target prediction parameter information.

[0038] In this embodiment, the prediction parameter information with the best prediction calculation effect is screened out from a large amount of initial prediction parameter information as the target prediction parameter information. The screening method can be to first quantify the effectiveness of each initial prediction parameter information in the prediction calculation process, and then screen according to the effectiveness.

[0039] Step S104 , calculating the number of casualties based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable.

[0040] In this embodiment, the preset information feature mapping function and the preset information feature displacement variables can be manually set and used to perform a nonlinear solution on earthquake intensity representation information, earthquake occurrence time information, and earthquake scene information. The earthquake intensity representation information, earthquake occurrence time information, and earthquake scene information can be first combined into a matrix. This matrix is ​​then transformed and calculated nonlinearly using the preset information feature mapping function and the preset information feature displacement variables in combination with the target prediction parameter information. The value obtained through the nonlinear analysis is the number of casualties.

[0041] Step S105 : calculating the material demand based on the earthquake scene emergency service information and the casualty information.

[0042] In this embodiment, the emergency service data may include the types of emergency supplies, the per capita demand for emergency supplies, the emergency supply satisfaction rate, and the lead time for supplying supplies. Based on the data including the types of emergency supplies, the per capita demand for emergency supplies, the emergency supply satisfaction rate, and the lead time for supplying supplies, a rescue supply demand forecasting model is constructed as follows:

[0043]

[0044] Among them, k represents the type of emergency supplies, such as drinking water, compressed biscuits, tents, etc. represents the per capita demand for emergency supplies of type k; It indicates the satisfaction rate of emergency material supply, that is, the degree to which material supply meets the needs of people in the disaster area. The service level coefficient corresponding to the case; LT represents the lead time of supply materials, that is, the lead time of supplying materials to the disaster area; It represents the quantity of emergency supplies k that should be supplied to the disaster site within a certain period of time; S(t) represents the number of survivors at time t; P(t) represents the number of injured at time t.

[0045] The material demand forecasting method provided in the embodiment of the present application has the following beneficial effects compared with the prior art: by analyzing and processing multiple influencing factors of earthquake disasters, the number of casualties is predicted and calculated, and based on the predicted number of casualties, the demand for rescue materials required after the disaster is predicted and calculated, and by screening parameters in the prediction and calculation process, the effective parameters after screening are combined with a preset nonlinear feature mapping function and the displacement variables of the features in the nonlinear space, the nonlinear features in the earthquake influencing factors are fully extracted and analyzed, thereby improving the accuracy and effectiveness of the post-disaster material demand forecast results.

[0046] Figure 2 The following is a flowchart of the material demand forecasting method provided in Example 2 of the present application, which differs from Example 1 above in that:

[0047] The initial prediction parameter information includes center parameter information, width parameter information and weight parameter information;

[0048] The step S103 specifically includes:

[0049] Step S201: Generate multiple first-generation individuals according to the center vector information, width parameter information and weight coefficient information.

[0050] In this embodiment, the numerical values ​​of the center vector information, width parameter information and weight coefficient information can be combined into a vector. A vector can contain three types of parameter information: center vector information, width parameter information and weight coefficient information. The vector can be used as a first-generation individual for subsequent screening calculations.

[0051] Step S202 : screening multiple first-generation individuals according to preset historical earthquake event record information, preset information feature mapping function, and preset information feature displacement variable to obtain current-generation individuals.

[0052] In this embodiment, the preset historical earthquake event record information can be obtained based on historical earthquake event record data, and can be obtained by crawling data on the Internet. The preset information feature mapping function can be a Gaussian function, a natural exponential function, or a hyperbolic tangent function. The preset information feature displacement variable can be set manually. The screening of multiple first-generation individuals can be performed by first calculating the fitness of multiple first-generation individuals through the preset historical earthquake event record information, the preset information feature mapping function, and the preset information feature displacement variable, and then screening according to the fitness of each first-generation individual. The first-generation individuals with high fitness have a high probability of being selected, and the screened individuals are used as the individuals of this generation for deep iterative calculation.

[0053] Step S203: performing crossover, mutation, and iterative selection calculations on the individuals of the current generation to generate individuals of the last generation.

[0054] In this embodiment, calculations can be performed on individuals of the current generation based on basic crossover and mutation operators. The crossover operator can be a single-point crossover, a two-point crossover / multi-point crossover, that is, two or more crossover points are randomly set in the chromosome code string, or a uniform crossover. The mutation operator can be a discrete recombination or a simulated binary crossover. After the chromosomes of the current generation population undergo crossover and mutation calculations, they become evolved individuals. By calculating the fitness of the evolved individuals, individuals with high fitness are selected from the evolved individuals as the next generation individuals for crossover and mutation calculations, thereby achieving iterative calculation of individuals to achieve optimal screening of individuals. When the fitness of all individuals is the same, the iterative calculation is stopped, and the individuals at this time are output as the last generation individuals.

[0055] Step S204: determining target prediction parameter information based on the last generation of individuals.

[0056] In this embodiment, it can be understood that multiple generations of individuals are calculated in the form of matrices or vectors, and the output last generation of individuals is also in the form of matrices or vectors, which requires format conversion to obtain target prediction parameter information.

[0057] The material demand forecasting method provided in the embodiment of the present application performs iterative calculations on individuals of the current generation generated by initial forecast parameters, performs preference selection on individuals of the current generation, and thereby selects individuals with the best forecasting effect for reproduction calculation, so that the settlement process can converge quickly, thereby quickly determining the individuals with the best forecasting calculation effect, and is used to improve the accuracy and effectiveness of forecasting the number of casualties and material demand.

[0058] Figure 3 The following is a flowchart of the material demand forecasting method provided in Example 3 of the present application, which differs from the above-mentioned Example 2 in that:

[0059] The preset historical earthquake event record information includes preset historical earthquake event intensity representation information, preset historical earthquake event occurrence time information, preset historical earthquake event scene information, and preset historical earthquake event death and casualty information;

[0060] The step S202 specifically includes:

[0061] Step S301, calculate multiple death and casualty prediction information based on preset historical earthquake event intensity characterization information, preset historical earthquake event occurrence time information, preset historical earthquake event scene information, preset information feature mapping function, preset information feature displacement variable and multiple initial individuals.

[0062] In this embodiment, based on the various parameters of the first-generation individuals, combined with the preset information feature mapping function and the preset information feature displacement variable, the input historical earthquake event intensity characterization information, the historical earthquake event occurrence time information and the historical earthquake event scene information can be fully analyzed and calculated to output multiple death and casualty prediction information, which can be used to subsequently quantify the prediction effect of the first-generation individuals, thereby quantitatively calculating the adaptability of the first-generation individuals.

[0063] Step S302 : calculating a plurality of prediction error values ​​based on the plurality of casualty prediction information and preset casualty information of historical earthquake events.

[0064] In this embodiment, the mean square error may be calculated based on a plurality of casualty prediction information and preset casualty information of historical earthquake events.

[0065] Step S303 , calculating the inverse of each prediction error value to obtain a representation value of the fitness level of each initial generation individual.

[0066] In this embodiment, the inverse of the mean square error may be used as a characterization value of the fitness level of the first-generation individuals to characterize the fitness level of the first-generation individuals, thereby facilitating subsequent in-depth screening operations on the first-generation individuals.

[0067] Step S304 : screening the individuals of the first generation according to the fitness representation value of each individual of the first generation to obtain individuals of the current generation.

[0068] In this embodiment, the selection probability of each first-generation individual can be calculated based on the characterization value of the first-generation individual's fitness level, and then the cumulative probability is calculated through the selection probabilities of all first-generation individuals, and then the first-generation individuals are extracted based on the cumulative probability of each first-generation individual, and the extracted individuals are used as the individuals of this generation.

[0069] The material demand forecasting method provided in the embodiment of the present application performs iterative calculations on individuals of the current generation and preferentially selects individuals with low prediction errors among all individuals of the current generation, so that the settlement process can converge quickly, thereby quickly solving the prediction parameters that make the prediction results with the highest accuracy in the prediction calculation process, and screening them out as target prediction parameters for subsequent prediction calculations, thereby improving the prediction accuracy of the number of casualties and the demand for disaster relief materials.

[0070] Figure 4 The following is a flowchart of the material demand forecasting method provided in Example 4 of the present application, which differs from Example 3 above in that:

[0071] The target prediction parameter information includes target center parameter information, target width parameter information and target weight parameter information;

[0072] The step S104 specifically includes:

[0073] Step S401 , respectively calculating the Euclidean distances of the earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, and target center parameter information.

[0074] In this embodiment, the Euclidean distances between the earthquake intensity characterization information and the target center parameter information, the earthquake occurrence time information and the target center parameter information, and the earthquake scene information and the target center parameter information are calculated respectively for subsequent prediction and calculation of the number of casualties.

[0075] Step S402 : Calculate a plurality of first prediction intermediate variable information according to the plurality of Euclidean distances and target width parameter information.

[0076] In this embodiment, the Euclidean distance and the target width parameter information can be squared respectively, and the result of the square of the Euclidean distance is used as the numerator, and the result of the square of the target width parameter information is used as the denominator, and the result of the fractional operation is negated, and the result after taking the negation is used as the independent variable of the natural exponential function, and the calculated function value is output as the first predicted intermediate variable information.

[0077] Step S403: Calculate multiple pieces of second prediction intermediate variable information based on the multiple pieces of first prediction intermediate variable information and a preset information feature mapping function.

[0078] In this embodiment, the first predicted intermediate variable information may be used as an independent variable of a preset information feature mapping function, and the calculated function value may be output as the second predicted intermediate variable information.

[0079] Step S404: performing weighted summation on a plurality of pieces of second prediction intermediate variable information according to the target weight parameter information to obtain third prediction intermediate variable information.

[0080] In this embodiment, it can be understood that the target weight parameter information includes multiple weight values, and the weight values ​​are used as weights for weighted summation. The second predicted intermediate variable information is weighted summed, and the calculation result is output as the third predicted intermediate variable information.

[0081] Step S405 , calculating the number of casualties based on the third prediction intermediate variable information and the preset information characteristic displacement variable.

[0082] In this embodiment, the third prediction intermediate variable information and the preset information characteristic displacement variable may be summed, and the obtained result may be output as the prediction result of the number of casualties information.

[0083] The material demand forecasting method provided in the embodiment of the present application effectively improves the nonlinear fitting capability and convergence speed in the forecasting process by combining target center parameter information, target width parameter information, and target weight parameter information with a preset information feature mapping function and a preset information feature displacement variable, thereby improving the efficiency and accuracy of the forecast of the number of deaths and casualties, and ensuring the timeliness and effectiveness of the subsequent calculation of the material demand.

[0084] Figure 5 The flowchart of the material demand forecasting method provided in the fifth embodiment of the present application is shown. The difference between the fifth embodiment and the first embodiment is that:

[0085] The initial prediction parameter information includes inversion calculation times information;

[0086] The step S103 specifically includes:

[0087] Step S501 : randomly extracting a plurality of inversion calculation times information to obtain a plurality of initial inversion times information set center points.

[0088] In this embodiment, the specific value and the number of randomly extracted inversion calculation times information can be random, and the extracted value is used as the center point of the initial inversion times information set for subsequent classification operations on the inversion calculation times information.

[0089] Step S502 , calculating the logical space distance between each of the inversion calculation times information and the center point of each initial inversion times information set.

[0090] In this embodiment, the logical space distance may be the Euclidean distance, which is used to subsequently divide the inversion calculation times information into corresponding sets, thereby achieving in-depth screening of the inversion calculation times information.

[0091] Step S503: Generate multiple inversion calculation number information sets according to the multiple logical space distances and the respective inversion calculation number information.

[0092] In this embodiment, the inversion calculation number information may be divided into sets to which the center point of the initial inversion number information set having the shortest logical spatial distance belongs, thereby generating multiple inversion number information sets.

[0093] Step S504: Calculate the mean of each of the inversion number information sets.

[0094] In this embodiment, the values ​​in all inversion number information sets are averaged, which serves as a basis for reselecting the center point of the inversion number information set to perform iterative screening calculations on the prediction parameter information.

[0095] Step S505: determining a plurality of center points of intermediate inversion number information sets according to the mean values ​​of the respective inversion number information sets.

[0096] In this embodiment, the value corresponding to the mean value may be used as the center point of the intermediate inversion number information set, or the value with the smallest difference from the mean value may be used as the center point of the intermediate inversion number information set.

[0097] Step S506, determining whether the center point of the intermediate inversion number information set is the same as the center point of the initial inversion number information set; if so, determining the center point of the intermediate inversion number information set as the target prediction parameter information; if not, determining the center point of each intermediate inversion number information set as the center point of the initial inversion number information set, and returning to step S502.

[0098] In this embodiment, when the center point of the intermediate inversion number information set is the same as the center point of the initial inversion number information set, it indicates that the current calculation of the center point of the inversion number information set has converged, and no further calculation of the center point of the inversion number information set is required. The set obtained by analyzing and calculating the center point of the inversion number information set is the global optimal solution. When the center point of the intermediate inversion number information set is different from the center point of the initial inversion number information set, it indicates that the current calculation of the center point of the inversion number information set has not converged, and further calculation of the center point of the inversion number information set is required until the center point of the intermediate inversion number information set is the same as the center point of the initial inversion number information set.

[0099] The material demand forecasting method provided in the embodiment of the present application calculates the center point of the inversion number information set to ensure that the center point of the inversion number information set after iterative calculation gradually approaches the global optimal solution. Through the individuals in the set where the center point of each inversion number information set is located, the most reasonable inversion number can be determined, which facilitates the subsequent settlement of various earthquake influencing factors, thereby ensuring the efficiency and accuracy of the calculation of material demand.

[0100] Figure 6 The following is a flowchart illustrating the material demand forecasting method provided in Example 6 of the present application, which differs from the above Example 5 in that:

[0101] The target prediction parameter information includes target inversion calculation times information;

[0102] The step S104 specifically includes:

[0103] Step S601 : Calculating characteristic mapping variable information according to the earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information characteristic mapping function, and a preset information characteristic displacement variable.

[0104] In this embodiment, the earthquake intensity characterization information, earthquake occurrence time information, and earthquake scene information can be first combined into a matrix, and then the matrix is ​​used as the independent variable of the preset information feature mapping function, and the calculated function value is summed with the preset information feature displacement variable, and the summation result is used as the feature mapping variable information.

[0105] Step S602 , calculating the number of casualties based on the target inversion calculation times information, characteristic mapping variable information, a preset information characteristic mapping function, and a preset information characteristic displacement variable.

[0106] In this embodiment, the characteristic mapping variable information can be used again as the independent variable of the information characteristic mapping function, and the recalculated function value is then summed with the information characteristic displacement variable to obtain the iterated characteristic mapping variable information. It can be understood that the number of iterations is determined based on the target number of inversion calculations, thereby achieving multiple inversion calculations for earthquake intensity representation information, earthquake occurrence time information, and earthquake scene information.

[0107] The material demand forecasting method provided in the embodiment of the present application performs multiple inversion operations on earthquake intensity characterization information, earthquake occurrence time information, and earthquake scene information based on the target inversion calculation number information, so that the nonlinear components in the earthquake intensity characterization information, earthquake occurrence time information, and earthquake scene information can be fully extracted and analyzed, thereby improving the accuracy of the prediction results, facilitating the subsequent accurate calculation of disaster relief material needs, and improving the effectiveness of earthquake disaster relief.

[0108] Corresponding to the method of the above embodiment, Figure 7 A structural block diagram of the material demand forecasting system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown. Figure 7 The exemplary material demand forecasting system may be an execution subject of the material demand forecasting method provided in the aforementioned first embodiment.

[0109] Reference Figure 7 , the material demand forecasting system includes:

[0110] Information acquisition module 710, used to obtain earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information and earthquake scene emergency service information;

[0111] An initial prediction parameter information generating module 720 is used to randomly generate a plurality of initial prediction parameter information;

[0112] The target prediction parameter information determination module 730 is used to screen the initial prediction parameter information and determine the target prediction parameter information;

[0113] a casualty information calculation module 740 for calculating casualty information based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable; and

[0114] The material demand calculation module 750 is used to calculate the material demand based on the earthquake scene emergency service information and the number of casualties information.

[0115] The process of each module realizing its own function in the material demand forecasting system provided in the embodiment of the present application can be specifically referred to the aforementioned Figure 1The description of the first embodiment is omitted here.

[0116] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0117] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0118] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0119] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0120] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish descriptions and should not be understood as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used in the text to describe various elements in some embodiments of the present application, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first table can be named a second table, and similarly, a second table can be named a first table without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.

[0121] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0122] The material demand forecasting method provided in the embodiments of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). The embodiments of the present application do not impose any restrictions on the specific types of terminal devices.

[0123] For example, the terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a TV set-top box (STB), customer premise equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network.

[0124] As an example and not a limitation, when the terminal device is a wearable device, the wearable device can also be a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are full-featured, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0125] Figure 8 This is a schematic diagram of the structure of a terminal device provided by an embodiment of the present application. Figure 8 As shown, the terminal device 8 of this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown), a memory 81, wherein the memory 81 stores a computer program 82 that can be run on the processor 80. When the processor 80 executes the computer program 82, the steps in the above-mentioned material demand forecasting method embodiments are implemented, such as Figure 1 Alternatively, when the processor 80 executes the computer program 82, the functions of the modules / units in the above-mentioned system embodiments are realized, for example, Figure 7 Functions of modules 710 to 750 are shown.

[0126] The terminal device 8 can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device can include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that Figure 8 It is only an example of the terminal device 8 and does not constitute a limitation of the terminal device 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include an input and sending device, a network access device, a bus, etc.

[0127] The processor 80 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0128] In some embodiments, the memory 81 may be an internal storage unit of the terminal device 8, such as a hard drive or memory of the terminal device 8. The memory 81 may also be an external storage device of the terminal device 8, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the terminal device 8. Furthermore, the memory 81 may include both an internal storage unit of the terminal device 8 and an external storage device. The memory 81 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 81 may also be used to temporarily store data that has been sent or is about to be sent.

[0129] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0130] An embodiment of the present application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, the terminal device implements the steps of any of the above-mentioned method embodiments.

[0131] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0132] An embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0133] If the integrated module / 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 storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or system that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0134] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0135] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0136] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0137] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A material demand forecasting method, characterized in that: include: Obtain earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, and earthquake scene emergency service information; Randomly generate multiple initial prediction parameter information; Screening the initial prediction parameter information to determine target prediction parameter information; Calculating the number of casualties based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable; Calculate the demand for supplies based on the earthquake scenario emergency service information and casualty information; The initial prediction parameter information includes inversion calculation times information; The step of screening the initial prediction parameter information to determine the target prediction parameter information specifically includes: Randomly extracting a plurality of inversion calculation times information to obtain a plurality of initial inversion times information set center points; Calculating the logical space distance between each of the inversion calculation number information and the center point of each initial inversion number information set; generating a plurality of inversion number information sets according to the plurality of logical space distances and the respective inversion calculation number information; Calculating the mean of each of the inversion number information sets; Determining a plurality of center points of intermediate inversion number information sets according to the mean values ​​of the respective inversion number information sets; Determining whether the center point of the intermediate inversion number information set is the same as the center point of the initial inversion number information set; If yes, the center point of the intermediate inversion number information set is determined as the target prediction parameter information; If not, taking the center point of each intermediate inversion number information set as the center point of the initial inversion number information set, and returning to the step of calculating the logical space distance between each inversion calculation number information and the center point of each initial inversion number information set; The target prediction parameter information includes target inversion calculation times information; The step of calculating the number of casualties based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable specifically includes: Calculating characteristic mapping variable information based on the earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information characteristic mapping function, and a preset information characteristic displacement variable; Calculating the number of casualties based on the target inversion calculation times, the characteristic mapping variable information, the preset information characteristic mapping function, and the preset information characteristic displacement variable; The logical space distance is the Euclidean distance, which is used to divide the inversion calculation times information into corresponding sets; Dividing the inversion calculation number information into the set to which the center point of the initial inversion number information set has the closest logical spatial distance, to generate multiple inversion number information sets; averaging the values ​​in all the inversion number information sets, and using this as a basis for reselecting the center point of the inversion number information set for iterative screening and calculation of the prediction parameter information; The value with the smallest difference from the mean is taken as the center point of the intermediate inversion number information set.

2. The material demand forecasting method according to claim 1, wherein: The earthquake intensity characterization information includes earthquake magnitude information and earthquake intensity information; The earthquake scenario information includes seismic fortification intensity information, housing damage degree representation information, forecast level representation information, disaster-affected population information, and population density information.

3. A material demand forecasting system, characterized in that: include: An information acquisition module is used to obtain earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, and earthquake scene emergency service information; An initial prediction parameter information generation module is used to randomly generate multiple initial prediction parameter information; a target prediction parameter information determination module, configured to screen the initial prediction parameter information and determine target prediction parameter information; a casualty information calculation module, configured to calculate casualty information based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable; as well as A material demand calculation module, configured to calculate material demand based on the earthquake scene emergency service information and casualty information; The initial prediction parameter information includes inversion calculation times information; The step of screening the initial prediction parameter information to determine the target prediction parameter information specifically includes: Randomly extracting a plurality of inversion calculation times information to obtain a plurality of initial inversion times information set center points; Calculating the logical space distance between each of the inversion calculation number information and the center point of each initial inversion number information set; generating a plurality of inversion number information sets according to the plurality of logical space distances and the respective inversion calculation number information; Calculating the mean of each of the inversion number information sets; Determining a plurality of center points of intermediate inversion number information sets according to the mean values ​​of the respective inversion number information sets; Determining whether the center point of the intermediate inversion number information set is the same as the center point of the initial inversion number information set; If yes, the center point of the intermediate inversion number information set is determined as the target prediction parameter information; If not, taking the center point of each intermediate inversion number information set as the center point of the initial inversion number information set, and returning to the step of calculating the logical space distance between each inversion calculation number information and the center point of each initial inversion number information set; The target prediction parameter information includes target inversion calculation times information; The step of calculating the number of casualties based on the target prediction parameter information, earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable specifically includes: Calculating characteristic mapping variable information based on the earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information characteristic mapping function, and a preset information characteristic displacement variable; Calculating the number of casualties based on the target inversion calculation times, the characteristic mapping variable information, the preset information characteristic mapping function, and the preset information characteristic displacement variable; The logical space distance is the Euclidean distance, which is used to divide the inversion calculation times information into corresponding sets; Dividing the inversion calculation number information into the set to which the center point of the initial inversion number information set has the closest logical spatial distance, to generate multiple inversion number information sets; averaging the values ​​in all the inversion number information sets, and using this as a basis for reselecting the center point of the inversion number information set for iterative screening and calculation of the prediction parameter information; The value with the smallest difference from the mean is taken as the center point of the intermediate inversion number information set.

4. A terminal device, characterized in that: The terminal device includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the method according to any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

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