Material demand prediction method and system

Abstract

The invention provides a material demand prediction method and system, and is suitable for the technical field of data processing, and the method comprises the steps: obtaining seismic intensity characterization information, seismic occurrence time information, seismic scene information, and seismic scene emergency service information; randomly generating a plurality of pieces of initial prediction parameter information; screening the initial prediction parameter information to determine target prediction parameter information; according to the target prediction parameter information, the earthquake intensity representation information, the earthquake occurrence time information, the earthquake scene information, a preset information feature mapping function and a preset information feature displacement variable, calculating dead casualty number information; and according to the earthquake scene emergency service information and the dead and casualty number information, calculating the material demand. The earthquake intensity characterization information and the earthquake scene emergency anti-disaster relief information are combined, the casualty number is rapidly predicted after the disaster, the material demand quantity is predicted and calculated, and the timeliness and effectiveness of earthquake post-disaster emergency rescue are improved.

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 reduce the negative impact of disasters and protect people’s lives and property.

[0003] In the existing technology, empirical judgments such as fuzzy comprehensive evaluation method, hierarchical analysis method, domino effect analysis, etc. are usually used to directly predict the demand for emergency rescue materials. However, the existing technology usually does not consider all the influencing factors, and it is difficult to collect effective cases that can deal with major earthquake disasters with strong suddenness, multiple factors, and multiple indicators in reality, so the prediction accuracy is often insufficient. Summary of the invention

[0004] In view of this, an embodiment of the present application provides a material demand forecasting method and system, aiming to solve the problem that the prior art is unable 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: 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; Calculate 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; The material demand is calculated based on the earthquake scene emergency service information and casualties information.

[0006] A second aspect of an embodiment of the present application provides a material demand forecasting system, including: 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 a plurality of initial prediction parameter information; A target prediction parameter information determination module is used to screen the initial prediction parameter information and determine the target prediction parameter information; a casualty information calculation module, configured to calculate the 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 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.

[0007] 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 executed on the processor, and when the processor executes the computer program, the steps of the material demand forecasting method described in the first aspect above are implemented.

[0008] 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.

[0009] 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 filtered valid parameters are combined with preset nonlinear feature mapping functions and displacement variables of features in nonlinear space, and the nonlinear features in earthquake influencing factors are fully extracted and analyzed, thereby improving the accuracy and effectiveness of the prediction results of post-disaster material demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative labor.

[0011] 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; 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; 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; 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; 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; 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; Figure 7 It is a structural diagram of a material demand forecasting system provided in an embodiment of the present application; Figure 8 It is a schematic diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also 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 prevent unnecessary details from obstructing the description of the present application.

[0013] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0014] 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: Step S101, obtaining earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information and earthquake scene emergency service information.

[0015] 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.

[0016] 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.

[0017] Step S102, randomly generate multiple initial prediction parameter information.

[0018] 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.

[0019] Step S103, screening the initial prediction parameter information to determine target prediction parameter information.

[0020] 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.

[0021] 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.

[0022] In this embodiment, the preset information feature mapping function and the preset information feature displacement variable can be artificially set, and are used to perform nonlinear solution on earthquake intensity characterization information, earthquake occurrence time information, and earthquake scene information. 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 ​​nonlinearly transformed and calculated by combining the preset information feature mapping function and the preset information feature displacement variable with the target prediction parameter information, and the value obtained after the nonlinear analysis calculation is the number of casualties information.

[0023] Step S105, calculating the material demand according to the earthquake scene emergency service information and the number of casualties information.

[0024] In this embodiment, the emergency service data may include the types of emergency supplies, the per capita demand for emergency supplies, the emergency supplies supply satisfaction rate, and the lead time for supplying supplies. A rescue supplies demand prediction model is constructed based on the types of emergency supplies, the per capita demand for emergency supplies, the emergency supplies supply satisfaction rate, and the lead time for supplying supplies as follows: 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 of emergency supplies; 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 for supplying materials, that is, the lead time for 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.

[0025] 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 filtered valid parameters are combined with preset nonlinear feature mapping functions and displacement variables of features in nonlinear space, so as to fully extract and analyze the nonlinear features in earthquake influencing factors, thereby improving the accuracy and effectiveness of the post-disaster material demand forecasting results.

[0026] Figure 2 The following is a flowchart of the material demand forecasting method provided in the second embodiment of the present application, which differs from the first embodiment described above in that: The initial prediction parameter information includes center parameter information, width parameter information and weight parameter information; The step S103 specifically includes: Step S201, generating a plurality of first-generation individuals according to the center vector information, width parameter information and weight coefficient information.

[0027] 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 include 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.

[0028] 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.

[0029] In this embodiment, the preset historical earthquake event record information can be obtained based on the 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 artificially. The screening of multiple first-generation individuals can be to first calculate 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 screen according to the fitness of each first-generation individual. The probability of the first-generation individual with a high fitness level being selected is high, and the screened individuals are used as the individuals of this generation for deep iterative calculation.

[0030] Step S203, performing crossover, mutation and iterative selection calculations on the individuals of the current generation to generate individuals of the last generation.

[0031] In this embodiment, the individuals of this generation can be calculated based on the basic crossover operator and mutation operator. 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 have undergone crossover calculation and mutation calculation, that is, the evolved individuals, the fitness of the evolved individuals is calculated, and the individuals with high fitness are selected from the evolved individuals as the next generation of individuals for crossover and mutation calculation, thereby realizing 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 of individuals.

[0032] Step S204, determining target prediction parameter information according to the last generation individuals.

[0033] In this embodiment, it can be understood that multiple generations of individuals are calculated in the form of a matrix or a vector, and the output last generation of individuals is also in the form of a matrix or a vector, and the target prediction parameter information can only be obtained after format conversion.

[0034] The material demand forecasting method provided in the embodiment of the present application performs iterative calculations on individuals of this generation generated by initial forecasting parameters, performs preference selection on individuals of this generation, and thereby selects individuals with the best forecasting effect for reproduction calculation, so that the settlement process can converge quickly, thereby quickly determining individuals with the best forecasting calculation effect, and is used to improve the accuracy and effectiveness of forecasting the number of deaths, casualties and material demand.

[0035] Figure 3The following is a flowchart of the material demand forecasting method provided in the third embodiment of the present application, which differs from the second embodiment in that: 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 number information; The step S202 specifically includes: 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 first-generation individuals.

[0036] In this embodiment, based on various parameters of the first-generation individuals, combined with a preset information feature mapping function and a preset information feature displacement variable, a full analysis and calculation can be performed on the input historical earthquake event intensity characterization information, historical earthquake event occurrence time information, and historical earthquake event scene information, so as to output multiple death and casualty prediction information, which can be used to quantify the prediction effect of the first-generation individuals in the future, thereby quantifying the adaptability of the first-generation individuals.

[0037] Step S302, calculating a plurality of prediction error values ​​according to the plurality of casualties prediction information and preset casualties information of historical earthquake events.

[0038] 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.

[0039] Step S303, calculating the inverse of each prediction error value to obtain a representation value of the fitness level of each initial generation individual.

[0040] 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, so as to facilitate subsequent in-depth screening operations on the first-generation individuals.

[0041] Step S304, screening the first-generation individuals according to the fitness representation value of each first-generation individual to obtain individuals of this generation.

[0042] In this embodiment, the selection probability of each first-generation individual can be calculated based on the characterization value of the fitness level of the first-generation individuals, and then the cumulative probability is calculated through the selection probabilities of all first-generation individuals, and then the first-generation individuals are extracted through the cumulative probabilities of each first-generation individual, and the extracted individuals are used as the individuals of this generation.

[0043] The material demand forecasting method provided in the embodiment of the present application performs iterative calculations on individuals of this generation and preferentially selects individuals with low prediction errors among all individuals of this generation, so that the settlement process can converge quickly, thereby quickly solving the prediction parameters that make the prediction results most accurate 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.

[0044] Figure 4 The flowchart of the material demand forecasting method provided in the fourth embodiment of the present application is shown, which differs from the third embodiment in that: The target prediction parameter information includes target center parameter information, target width parameter information and target weight parameter information; The step S104 specifically includes: 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.

[0045] In this embodiment, the Euclidean distances between earthquake intensity characterization information and target center parameter information, earthquake occurrence time information and target center parameter information, and earthquake scene information and target center parameter information are calculated respectively for subsequent prediction and calculation of the number of casualties.

[0046] Step S402: Calculate a plurality of first prediction intermediate variable information according to the plurality of Euclidean distances and target width parameter information.

[0047] In this embodiment, the Euclidean distance and the target width parameter information may be squared respectively, and the squared Euclidean distance is used as the numerator, and the squared target width parameter information is used as the denominator. The result of the fractional operation is negated, and the negated result is used as the independent variable of the natural exponential function. The calculated function value is output as the first predicted intermediate variable information.

[0048] Step S403: Calculate multiple pieces of second prediction intermediate variable information according to the multiple pieces of first prediction intermediate variable information and a preset information feature mapping function.

[0049] In this embodiment, the first prediction 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 prediction intermediate variable information.

[0050] Step S404: performing weighted summation on a plurality of pieces of the second prediction intermediate variable information according to the target weight parameter information to obtain third prediction intermediate variable information.

[0051] 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, and the second predicted intermediate variable information is weighted summed, and the calculation result is output as the third predicted intermediate variable information.

[0052] Step S405, calculating the number of casualties based on the third prediction intermediate variable information and the preset information characteristic displacement variable.

[0053] 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.

[0054] The material demand forecasting method provided in the embodiment of the present application combines 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 effectively improving the nonlinear fitting capability in the forecasting process and improving the convergence speed, thereby improving the prediction efficiency and accuracy of the number of deaths and casualties, and ensuring the timeliness and effectiveness of subsequent calculations of material demand.

[0055] Figure 5 The flowchart of the material demand forecasting method provided in the fifth embodiment of the present application is shown, which differs from the first embodiment described above in that: The initial prediction parameter information includes inversion calculation times information; The step S103 specifically includes: Step S501, randomly extracting multiple inversion calculation times information to obtain multiple initial inversion times information set center points.

[0056] 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.

[0057] 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.

[0058] In this embodiment, the logical space distance may be the Euclidean distance, which is used to subsequently divide the inversion calculation number information into sets to which it belongs, thereby achieving in-depth screening of the inversion calculation number information.

[0059] Step S503: Generate multiple inversion number information sets according to the multiple logical space distances and the respective inversion calculation number information.

[0060] 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 space distance belongs, thereby generating multiple inversion number information sets.

[0061] Step S504, calculating the mean of each of the inversion number information sets.

[0062] In this embodiment, the values ​​in all the inversion number information sets are averaged, which is used as a basis for reselecting the center point of the inversion number information set to perform iterative screening calculations on the prediction parameter information.

[0063] Step S505: determining a plurality of center points of intermediate inversion number information sets according to the mean values ​​of the inversion number information sets.

[0064] 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.

[0065] 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.

[0066] 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 means that the current calculation of the center point of the inversion number information set has converged, and there is no need to further calculate the center point of the inversion number information set. 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 means that the current calculation of the center point of the inversion number information set has not converged, and it is necessary to further calculate the center point of the inversion number information set 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.

[0067] The material demand prediction 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, and the most reasonable inversion number can be determined through the individuals in the set where the center point of each inversion number information set is located, which is convenient for the subsequent settlement of various earthquake influencing factors, thereby ensuring the efficiency and accuracy of the calculation of material demand.

[0068] Figure 6The flowchart of the material demand forecasting method provided in the sixth embodiment of the present application is shown, which differs from the fifth embodiment described above in that: The target prediction parameter information includes target inversion calculation times information; The step S104 specifically includes: Step S601, calculating feature mapping variable information according to the earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function and a preset information feature displacement variable.

[0069] 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 sum result is used as the feature mapping variable information.

[0070] Step S602, calculating the number of casualties according to the target inversion calculation times information, feature mapping variable information, a preset information feature mapping function and a preset information feature displacement variable.

[0071] In this embodiment, the characteristic mapping variable information can be used as the independent variable of the information characteristic mapping function again, and then the function value calculated again is 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 according to the target inversion calculation number information, so as to realize multiple inversion calculations of earthquake intensity characterization information, earthquake occurrence time information, and earthquake scene information.

[0072] 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 prevention and disaster relief.

[0073] Corresponding to the method of the above embodiment, Figure 7 A structural block diagram of a 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.

[0074] Reference Figure 7 , the material demand forecasting system includes: The information acquisition module 710 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 generating module 720 is used to randomly generate a plurality of initial prediction parameter information; A target prediction parameter information determination module 730 is used to screen the initial prediction parameter information to determine the target prediction parameter information; The casualty information calculation module 740 is used to calculate the casualty information according to 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 The material demand calculation module 750 is used to calculate the material demand according to the earthquake scene emergency service information and the number of casualties information.

[0075] 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 in the above Figure 1 The description of the first embodiment is not repeated here.

[0076] 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 the present application.

[0077] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0078] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0079] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" 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 "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0080] 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 cannot be understood as indicating or suggesting relative importance. It should also be understood that although the terms "first", "second", etc. are used to describe various elements in some embodiments of the present application in the text, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first table can be named as the second table, and similarly, the second table can be named as the first table without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0081] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0082] The material demand forecasting method provided in the embodiment 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 (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiment of the present application does not impose any restrictions on the specific type of terminal devices.

[0083] 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), a customer premises 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, etc.

[0084] As an example but not 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 just hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, 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.

[0085] Figure 8 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 in the figure), 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 each module / unit in the above-mentioned system embodiments are realized, for example, Figure 7Functions of modules 710 to 750 are shown.

[0086] The terminal device 8 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will appreciate 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 sending device, a network access device, a bus, etc.

[0087] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0088] In some embodiments, the memory 81 may be an internal storage unit of the terminal device 8, such as a hard disk 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 disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 8. Further, the memory 81 may also include both an internal storage unit and an external storage device of the terminal device 8. The memory 81 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 81 may also be used to temporarily store data that has been sent or is to be sent.

[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0090] 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, wherein when the processor executes the computer program, the terminal device implements the steps in any of the above-mentioned method embodiments.

[0091] 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 method embodiments can be implemented.

[0092] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0093] 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, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or system that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0094] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0095] Those of ordinary skill 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 to be beyond the scope of this application.

[0096] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0097] The embodiments described above 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, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope 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; Calculate 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; The material demand is calculated based on the earthquake scene emergency service information and casualties information.

2. The material demand forecasting method according to claim 1, characterized in that: 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. The material demand forecasting method according to claim 1, characterized in that: The initial prediction parameter information includes center parameter information, width parameter information and weight parameter information; The step of screening the initial prediction parameter information to determine the target prediction parameter information specifically includes: Generate multiple first-generation individuals according to the center vector information, width parameter information and weight coefficient information; According to preset historical earthquake event record information, preset information feature mapping function and preset information feature displacement variable, multiple first-generation individuals are screened to obtain current-generation individuals; Performing crossover, mutation, and iterative selection calculations on the individuals of the current generation to generate individuals of the last generation; According to the last generation individuals, target prediction parameter information is determined.

4. The material demand forecasting method according to claim 3, characterized in that: 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 number information; The step of screening a plurality of first-generation individuals according to preset historical earthquake event record information, preset information feature mapping function and preset information feature displacement variable to obtain individuals of this generation specifically includes: Calculate multiple 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 first-generation individuals; Calculating a plurality of prediction error values ​​according to the plurality of predicted casualties information and preset casualties information of historical earthquake events; Calculate the reciprocal of each prediction error value to obtain a representation value of the fitness level of each initial generation individual; The individuals of the first generation are screened according to the fitness characterization values ​​of each of the individuals of the first generation to obtain individuals of this generation.

5. The material demand forecasting method according to claim 3, characterized in that: The target prediction parameter information includes target center parameter information, target width parameter information and target weight parameter 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: Calculate the Euclidean distance of the earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information and target center parameter information respectively; Calculating a plurality of first prediction intermediate variable information according to the plurality of Euclidean distances and target width parameter information; Calculating a plurality of second prediction intermediate variable information according to the plurality of first prediction intermediate variable information and a preset information feature mapping function; According to the target weight parameter information, weighted summing is performed on a plurality of the second prediction intermediate variable information to obtain third prediction intermediate variable information; The number of casualties is calculated based on the third prediction intermediate variable information and the preset information characteristic displacement variable.

6. The material demand forecasting method according to claim 1, characterized in that: 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 the inversion calculation times information to obtain a plurality of initial inversion times information set center points; Calculating the logical space distances of the center points of each inversion calculation number information and each initial inversion number information set; Generate multiple inversion number information sets according to the multiple 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 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 times information set is determined as the target prediction parameter information; If not, the center point of each intermediate inversion number information set is used as the center point of the initial inversion number information set, and the process returns 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.

7. The material demand forecasting method according to claim 6, characterized in that: 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 feature mapping variable information according to the earthquake intensity characterization information, earthquake occurrence time information, earthquake scene information, a preset information feature mapping function, and a preset information feature displacement variable; The number of casualties is calculated based on the target inversion calculation times information, the characteristic mapping variable information, the preset information characteristic mapping function and the preset information characteristic displacement variable.

8. 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 a plurality of initial prediction parameter information; A target prediction parameter information determination module is used to screen the initial prediction parameter information and determine the target prediction parameter information; A casualty information calculation module is used to calculate the 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 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.

9. 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 7 are implemented.

10. 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 7 are implemented.

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