Innovation and entrepreneurship simulation method and system based on big data

Abstract

The invention relates to the technical field of entrepreneurship simulation, and particularly discloses an innovation entrepreneurship simulation method and system based on big data, and the method comprises the steps: obtaining market fluctuation parameters in a preset time range, and determining the probability of market fluctuation at each moment according to the market fluctuation parameters; opening an information simulation port, receiving simulation information input by a user based on the information simulation port, comparing the simulation information with cost input information of existing enterprises, matching similar enterprises, reading asset information of the similar enterprises, and taking the asset information as a simulation result; randomly introducing the market fluctuation based on the probability of generating the market fluctuation at each moment, and correcting a simulation result; according to the method, all fluctuations in a period of time are counted, the fluctuations are periodically analyzed, then a probability function is built for each fluctuation, and in the simulation process, the fluctuations are randomly generated based on the probability functions, so that the randomness of the simulation process is greatly improved, and the integrating degree with the actual situation is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of entrepreneurship simulation, and in particular to an innovation and entrepreneurship simulation method and system based on big data. Background Art

[0002] The innovation and entrepreneurship simulation platform is a tool or software system based on a virtual environment, which aims to help users experience and practice the whole process of innovation and entrepreneurship by simulating the real-world business environment. It usually provides users with a dynamic virtual market where users can create businesses, launch products, formulate strategies, conduct financial management, compete with other virtual companies, respond to market changes, etc. Many games have similar modules.

[0003] Most existing innovation and entrepreneurship simulation platforms provide services according to fixed rules. For example, different inputs correspond to what kind of outputs, and what kind of adverse effects are produced at what stage. These are all determined by some preset functional relationships and have very low randomness. However, the randomness of the actual situation is extremely strong, especially the market volatility factors. How to improve the randomness in the innovation and entrepreneurship simulation platform is the technical problem that the technical solution of the present invention aims to solve. Summary of the invention

[0004] The purpose of the present invention is to provide an innovation and entrepreneurship simulation method and system based on big data to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A big data-based innovation and entrepreneurship simulation method, the method comprising: According to the preset ratio, enterprises of different fields and sizes are determined, information query requests are sent to the enterprises, and cost input information containing time information and asset information containing time information are received from the enterprises; Obtaining market fluctuation parameters within a preset time range, and determining the probability of market fluctuations occurring at each moment according to the market fluctuation parameters; the market fluctuation parameters include market fluctuation types and occurrence moments; An information simulation port is opened, and simulation information input by a user is received based on the information simulation port, and the simulation information is compared with cost input information containing time information, similar enterprises are matched, and asset information containing time information of similar enterprises is read as a simulation result; Market fluctuations are randomly introduced based on the probability of market fluctuations at each moment, and the simulation results are corrected.

[0006] As a further solution of the present invention, the steps of determining enterprises of different fields and sizes according to a preset ratio, sending information query requests to the enterprises, and receiving cost input information containing time information and asset information containing time information fed back by the enterprises include: Obtaining professional information of the simulated group, and determining the enterprise field according to the professional information; receiving two geometric series set by the management party as the enterprise scale series and the enterprise quantity series respectively; the first term, the last term and the common ratio of the geometric series are preset values; For any data in the enterprise scale series, query the enterprise based on the data, send an information query request to the enterprise, and receive the cost input information containing time information and asset information containing time information fed back by the enterprise; Record the number of enterprises that provide feedback information, and when the number meets the enterprise number sequence, stop the information acquisition process; Among them, the cost investment information is an array set, each array in the array set contains a time tag, each element in each array corresponds to the investment type one by one, and the element value represents the investment amount; the asset information containing time information is also an array set, each array in the array set contains a time tag, each element in each array corresponds to the asset type one by one, and the element value represents the asset amount.

[0007] As a further solution of the present invention, the step of determining the probability of market fluctuations occurring at each moment according to the market fluctuation parameters based on the market fluctuation parameters obtained within the preset time range includes: The time frame for receiving the management input; Get the moment of occurrence of each market movement type within the time range; Count the occurrence time of each market fluctuation type in chronological order and create a zero-one sequence; one means it occurs, and zero means it does not occur; The probability of each type of market fluctuation occurring at each moment is determined based on the zero-one sequence.

[0008] As a further solution of the present invention, the step of determining the probability of each type of market fluctuation occurring at each moment according to the zero-one sequence comprises: For any zero-one sequence of market fluctuation type, perform discrete Fourier transform to obtain frequency components of different frequencies; Selecting characteristic frequency components according to the amplitudes of frequency components of different frequencies; Determine the fluctuation period according to the frequency of the selected characteristic frequency component; Determine the probability of generating the market fluctuation type based on the fluctuation period; The process of determining the probability is: ; In the formula, Indicates the probability of a certain type of market fluctuation, is the preset correction factor, represents the number of selected characteristic frequency components, It means that after sorting the characteristic frequency components in descending order based on the amplitude, The amplitude of the characteristic frequency component, Indicates The period corresponding to the characteristic frequency components; Indicates the time when this type of market fluctuation last occurred. Indicates how long it has been since this type of market volatility last occurred. Updated every time a market volatility type occurs, Returns to zero each time a market movement type occurs.

[0009] As a further solution of the present invention, the open information simulation port receives simulation information input by a user based on the information simulation port, compares the simulation information with cost input information containing time information, matches similar enterprises, and reads asset information containing time information of similar enterprises as the simulation result, and the steps include: Opening an information simulation port, and receiving cost input information input by a user based on the information simulation port; Compare the cost input information entered by the user with the cost input information of the enterprise containing time information to match similar enterprises; Read asset information containing time information of similar enterprises, determine the asset information at the current moment, and feedback to the user.

[0010] As a further solution of the present invention, the step of randomly introducing market fluctuations based on the probability of market fluctuations at each moment and correcting the simulation results comprises: Query the probability of various market fluctuation types occurring at each moment; Randomly introduce market fluctuation types based on the probability, input the market fluctuation types into a preset impact prediction model, and output a correction coefficient; Correcting the asset information at the current moment according to the correction coefficient; Among them, the impact prediction model is a mapping of market volatility types and the degree of impact on different assets.

[0011] The technical solution of the present invention also provides an innovation and entrepreneurship simulation system based on big data, the system comprising: An information pre-acquisition module is used to determine enterprises of different fields and sizes according to a preset ratio, send information query requests to the enterprises, and receive cost input information containing time information and asset information containing time information fed back by the enterprises; A fluctuation statistics module is used to obtain market fluctuation parameters within a preset time range, and determine the probability of market fluctuations occurring at each moment according to the market fluctuation parameters; the market fluctuation parameters include market fluctuation types and occurrence times; An enterprise simulation module is used to open an information simulation port, receive simulation information input by a user based on the information simulation port, compare the simulation information with cost input information containing time information, match similar enterprises, and read asset information containing time information of similar enterprises as a simulation result; The volatility introduction module is used to randomly introduce market fluctuations based on the probability of market fluctuations at each moment and to correct the simulation results.

[0012] As a further solution of the present invention, the information pre-acquisition module includes: A field determination unit, used to obtain professional information of the simulated group and determine the enterprise field according to the professional information; A sequence creation unit is used to receive two geometric sequence set by the management party, which are used as the enterprise scale sequence and the enterprise quantity sequence respectively; the first term, the last term and the common ratio of the geometric sequence are preset values; An information query unit is used to query the enterprise based on any data in the enterprise scale series, send an information query request to the enterprise, and receive cost input information containing time information and asset information containing time information fed back by the enterprise; A process management unit, used to record the number of enterprises that have provided feedback information, and when the number meets the enterprise number series, stop the information acquisition process; Among them, the cost investment information is an array set, each array in the array set contains a time tag, each element in each array corresponds to the investment type one by one, and the element value represents the investment amount; the asset information containing time information is also an array set, each array in the array set contains a time tag, each element in each array corresponds to the asset type one by one, and the element value represents the asset amount.

[0013] As a further solution of the present invention, the fluctuation statistics module includes: A time range receiving unit, used to receive a time range input by a management party; Amplitude acquisition unit, used to obtain the occurrence moment of each market fluctuation type within the time range; The sequence creation unit is used to count the occurrence time of each market fluctuation type according to the time sequence and create a zero-one sequence; one means occurrence, and zero means no occurrence; The probability determination unit is used to determine the probability of each type of market fluctuation occurring at each moment according to the zero-one sequence.

[0014] As a further solution of the present invention, the probability determination unit includes: The transformation subunit is used to perform discrete Fourier transformation on the zero-one sequence of any market fluctuation type to obtain frequency components of different frequencies; A selection subunit is used to select characteristic frequency components according to the amplitudes of frequency components of different frequencies; A period determination subunit, used to determine the fluctuation period according to the frequency of the selected characteristic frequency component; A cycle application subunit, used for determining the probability of generating the market fluctuation type based on the fluctuation cycle; The process of determining the probability is: ; In the formula, Indicates the probability of a certain type of market fluctuation, is the preset correction factor, represents the number of selected characteristic frequency components, It means that after sorting the characteristic frequency components in descending order based on the amplitude, The amplitude of the characteristic frequency component, Indicates The period corresponding to the characteristic frequency components; Indicates the time when this type of market fluctuation last occurred. Indicates how long it has been since this type of market volatility last occurred. Updated every time a market volatility type occurs, Returns to zero each time a market volatility type occurs.

[0015] Compared with the prior art, the beneficial effects of the present invention are: the present invention counts all fluctuations within a period of time, performs periodic analysis on the fluctuations, and then builds a probability function for each fluctuation. During the simulation process, fluctuations are randomly generated based on the probability function, which greatly improves the randomness of the simulation process and is more consistent with the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0017] Figure 1 This is a flowchart of the innovation and entrepreneurship simulation method based on big data.

[0018] Figure 2 This is the structural block diagram of the innovation and entrepreneurship simulation system based on big data. DETAILED DESCRIPTION

[0019] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] Figure 1 The flowchart of the innovation and entrepreneurship simulation method based on big data is as follows. In an embodiment of the present invention, a method for simulating innovation and entrepreneurship based on big data includes: Step S100: determining enterprises of different fields and sizes according to a preset ratio, sending information query requests to the enterprises, and receiving cost input information containing time information and asset information containing time information fed back by the enterprises; In the process of building an innovation and entrepreneurship simulation platform, data feeding must be carried out first. Step S100 is the data feeding process. According to a preset ratio, some enterprises are obtained as reference enterprises. The more reference enterprises there are and the more types they are, the more accurate the entrepreneurship simulation results will be. Specifically, the ratio of different enterprises needs to be determined by the management party. Enterprises in different fields and sizes are determined according to the preset ratio. Some enterprises will provide data, while others will not. Therefore, this is a request process. If the enterprise is willing to provide data, the cost input information containing time information and the asset information containing time information fed back by the enterprise are received. The cost input information containing time information is used to characterize what kind of cost the enterprise has invested at what time, and the asset information containing time information is used to characterize what kind of assets the enterprise has at what time. The assets include real estate and movable property.

[0021] Step S200: Obtain market fluctuation parameters within a preset time range, and determine the probability of market fluctuations occurring at each moment according to the market fluctuation parameters; the market fluctuation parameters include market fluctuation types and occurrence moments; The unique point of the technical solution of the present invention is that it introduces market volatility factors, which include economic factors, supply and demand factors, environmental factors, etc., obtains market volatility parameters within a preset time range (such as three years), determines how many market fluctuations exist within the time range and what the corresponding market fluctuation types are, and analyzes these market fluctuations to determine the probability of various market fluctuations occurring at the current moment.

[0022] Step S300: opening an information simulation port, receiving simulation information input by a user based on the information simulation port, comparing the simulation information with cost input information containing time information, matching similar enterprises, and reading asset information containing time information of similar enterprises as simulation results; Step S300 is a specific simulation process, which opens an information simulation port to receive simulation information input by the user. The simulation information is how much cost is invested at each moment. This is common on existing entrepreneurial simulation platforms, and even some games have related operations. The simulation information is compared with the data of each enterprise counted in step S100 to match similar enterprises. The asset information containing time information of similar enterprises can be used as the simulation result.

[0023] It should be noted that, regarding the time information in the above-mentioned comparison process, the time information of all the enterprises in this application is the relative time relative to the time when the enterprise is just established, that is, taking the time when the enterprise is just established as the zero time point, when comparing the simulation information with the cost input information containing time information, it is necessary to obtain the time of the simulation information, such as half a year, and then, when comparing, it is necessary to intercept the cost input information of all enterprises within half a year, and then compare them. This is the time domain alignment process.

[0024] Step S400: randomly introducing market fluctuations based on the probability of market fluctuations at each moment, and correcting the simulation results; Since the present application also counts and analyzes market volatility parameters, randomly introduces market fluctuations based on the probability of market fluctuations at each moment, corrects the simulation results, and increases randomness.

[0025] Regarding step S100, the steps of determining enterprises of different fields and sizes according to a preset ratio, sending information query requests to the enterprises, and receiving cost input information containing time information and asset information containing time information fed back by the enterprises include: Obtaining professional information of the simulated group, and determining the enterprise field according to the professional information; receiving two geometric series set by the management party as the enterprise scale series and the enterprise quantity series respectively; the first term, the last term and the common ratio of the geometric series are preset values; For any data in the enterprise scale series, query the enterprise based on the data, send an information query request to the enterprise, and receive the cost input information containing time information and asset information containing time information fed back by the enterprise; Record the number of enterprises that provide feedback information, and when the number meets the enterprise number sequence, stop the information acquisition process; The above content describes the data pre-acquisition process of the enterprise. First, the innovation and entrepreneurship simulation platform is generally used in sharing scenarios. The scenario for receiving sharing is generally a similar group, such as students of a certain major, that is, the simulated group in the above content, to obtain the professional information of the simulated group and determine the enterprise field based on the professional information; then, the management party sets two geometric series, which are the enterprise scale series and the enterprise quantity series respectively. The enterprise scale series is used to determine the scales of the enterprises to obtain data, and the enterprise quantity series is used to characterize how many enterprises of each scale need to be obtained; each time an enterprise is selected, an information query request is sent to the enterprise, and the cost input information containing time information and the asset information containing time information fed back by the enterprise are received; the cost input information is an array set, each array in the array set contains a time tag, each element in each array corresponds to an input type one by one, and the element value represents the input amount; the asset information containing time information is also an array set, each array in the array set contains a time tag, each element in each array corresponds to an asset type one by one, and the element value represents the asset amount; the cost input information and asset information are the characteristics of each enterprise, so the above content defines the specific data structure.

[0026] Regarding step S200, the step of determining the probability of market fluctuations occurring at each moment according to the market fluctuation parameters based on the market fluctuation parameters acquired within the preset time range includes: The time frame for receiving the management input; Get the moment of occurrence of each market movement type within the time range; Count the occurrence time of each market fluctuation type in chronological order and create a zero-one sequence; one means it occurs, and zero means it does not occur; The probability of each type of market fluctuation occurring at each moment is determined based on the zero-one sequence.

[0027] The above content limits the analysis process of market volatility parameters. The management party sets the time range, which is used to characterize the statistical period of market volatility. There are many types of market fluctuations, such as various economic measures and changes in the natural environment. Factors that can affect the market can all be used as market volatility types. For each type of market fluctuation, its occurrence time within the time range is obtained, and the occurrence time of each type of market fluctuation is counted according to the time sequence to obtain a zero-one sequence. The element number in the zero-one sequence corresponds to the time, and the element value includes zero and one. One indicates occurrence and zero indicates non-occurrence. The zero-one sequence indicates how often the market fluctuation type occurs.

[0028] Specifically, the step of determining the probability of each type of market fluctuation occurring at each moment according to the zero-one sequence includes: For any zero-one sequence of market fluctuation type, perform discrete Fourier transform to obtain frequency components of different frequencies; Selecting characteristic frequency components according to the amplitudes of frequency components of different frequencies; Determine the fluctuation period according to the frequency of the selected characteristic frequency component; Determine the probability of generating the market fluctuation type based on the fluctuation period; Different types of market fluctuations are analyzed separately, and a discrete Fourier transform is performed on the zero-one sequence of any market fluctuation type to obtain frequency components of different frequencies. The fluctuation period is determined based on the frequency of the selected characteristic frequency components. Generally, one or several frequency components with the largest amplitude are selected, and the fluctuation period is determined based on these frequency components. The product of frequency and period is fixed, and the conversion process is very simple. Finally, the probability of generating this type of market fluctuation can be determined based on the fluctuation period.

[0029] The process of determining the probability is: ; In the formula, Indicates the probability of a certain type of market fluctuation, is the preset correction factor, represents the number of selected characteristic frequency components, It means that after sorting the characteristic frequency components in descending order based on the amplitude, The amplitude of the characteristic frequency component, Indicates The period corresponding to the characteristic frequency components; Indicates the time when this type of market fluctuation last occurred. Indicates how long it has been since this type of market volatility last occurred. Updated every time a market volatility type occurs, Returns to zero each time a market movement type occurs.

[0030] The calculation process of probability is explained as follows: For a certain type of market fluctuation, the probability is reset to zero each time it occurs, and then, as time goes by, the possibility of its occurrence increases. Since the zero-one sequence of the same type of market fluctuation may have multiple cycles (the number of characteristic frequency components), each cycle needs to be analyzed separately, and then the probabilities are added together. Furthermore, the characteristic frequency components with larger amplitudes have higher importance, and accordingly, the weight when adding them should be larger, that is, item.

[0031] In addition, regarding the process of selecting characteristic frequency components according to the amplitudes of frequency components of different frequencies, the characteristic frequency components are generally arranged in descending order according to the amplitudes, and a preset number of frequency components, such as three, are selected from the beginning as characteristic frequency components.

[0032] Furthermore, regarding the explanation of probability, under normal circumstances, the sum of the probabilities of multiple situations of the same event should be one, but the probability of this application is not like this. Each type of market fluctuation is independent, and the sum of its occurrence and non-occurrence is one, not the sum of the probabilities of all types of market fluctuations. This also means that multiple types of market fluctuations may occur continuously in the same period of time; and the probability of a market fluctuation type occurring itself is not too high, which is determined by the parameters in the above content. To control, Used to ensure that the probability is at a low value, which is pre-set by the administrator.

[0033] Regarding step S300, the information simulation port is opened, simulation information input by the user is received based on the information simulation port, the simulation information is compared with the cost input information containing time information, similar enterprises are matched, and asset information containing time information of similar enterprises is read, and the steps as simulation results include: Opening an information simulation port, and receiving cost input information input by a user based on the information simulation port; Compare the cost input information entered by the user with the cost input information of the enterprise containing time information to match similar enterprises; Read asset information containing time information of similar enterprises, determine the asset information at the current moment, and feedback to the user.

[0034] The above content specifically defines step S300, which is a usage process. The simulation information is limited to the cost investment information input by the user. When the user creates an enterprise on this platform, the timing starts. When the user enters the cost investment information, there must be an investment time. The difference between the investment time and the zero time is calculated, and then the cost investment information containing time information of all enterprises is intercepted according to the difference. When the time is the same, the subsequent comparison process can be carried out at this time. Since the present application has limited the cost investment information to an array, the cost investment information within the same period is limited, which is a collection of multiple arrays. By comparing the array collection, similar enterprises can be matched; finally, the asset information containing time information of similar enterprises is read, and the asset information of the most recent time is queried according to the current time, and the result is fed back to the user as a simulation result.

[0035] Regarding step S400, the step of randomly introducing market fluctuations based on the probability of market fluctuations at each moment and correcting the simulation results includes: Query the probability of various market fluctuation types occurring at each moment; Randomly introduce market fluctuation types based on the probability, input the market fluctuation types into a preset impact prediction model, and output a correction coefficient; Correcting the asset information at the current moment according to the correction coefficient; Among them, the impact prediction model is a mapping of market volatility types and the degree of impact on different assets.

[0036] In an example of the technical solution of the present invention, any market fluctuation type and the impact of the market fluctuation type on any type of enterprise are first obtained as a correction coefficient, the market fluctuation type is used as input, and the correction coefficient is used as output to construct a sample set, and the neural network model is trained. When the number of samples in the sample set is large enough, the error rate of the neural network model is extremely small. When the error rate is less than a preset threshold, the neural network model is output as an impact prediction model.

[0037] When in use, the market fluctuation type is randomly introduced based on the probability, the market fluctuation type is input into the preset impact prediction model, and the correction coefficient is output. The asset information at the current moment is corrected according to the correction coefficient. The correction method is generally to use the product method to enlarge or reduce the asset amount.

[0038] like Figure 2 As shown, the technical solution of the present invention also provides an innovation and entrepreneurship simulation system based on big data, and the system 10 includes: The information pre-acquisition module 11 is used to determine enterprises of different fields and sizes according to a preset ratio, send information query requests to the enterprises, and receive cost input information containing time information and asset information containing time information fed back by the enterprises; The fluctuation statistics module 12 is used to obtain market fluctuation parameters within a preset time range, and determine the probability of market fluctuations occurring at each moment according to the market fluctuation parameters; the market fluctuation parameters include market fluctuation types and occurrence moments; The enterprise simulation module 13 is used to open an information simulation port, receive simulation information input by a user based on the information simulation port, compare the simulation information with the cost input information containing time information, match similar enterprises, and read the asset information containing time information of similar enterprises as a simulation result; The volatility introduction module 14 is used to randomly introduce market volatility based on the probability of market volatility at each moment, and to correct the simulation results.

[0039] Furthermore, the information pre-acquisition module 11 includes: A field determination unit, used to obtain professional information of the simulated group and determine the enterprise field according to the professional information; A sequence creation unit is used to receive two geometric sequence set by the management party, which are used as the enterprise scale sequence and the enterprise quantity sequence respectively; the first term, the last term and the common ratio of the geometric sequence are preset values; An information query unit is used to query the enterprise based on any data in the enterprise scale series, send an information query request to the enterprise, and receive cost input information containing time information and asset information containing time information fed back by the enterprise; A process management unit, used to record the number of enterprises that have provided feedback information, and when the number meets the enterprise number series, stop the information acquisition process; Among them, the cost investment information is an array set, each array in the array set contains a time tag, each element in each array corresponds to the investment type one by one, and the element value represents the investment amount; the asset information containing time information is also an array set, each array in the array set contains a time tag, each element in each array corresponds to the asset type one by one, and the element value represents the asset amount.

[0040] Specifically, the fluctuation statistics module 12 includes: A time range receiving unit, used to receive a time range input by a management party; Amplitude acquisition unit, used to obtain the occurrence moment of each market fluctuation type within the time range; The sequence creation unit is used to count the occurrence time of each market fluctuation type according to the time sequence and create a zero-one sequence; one means occurrence, and zero means no occurrence; The probability determination unit is used to determine the probability of each type of market fluctuation occurring at each moment according to the zero-one sequence.

[0041] Wherein, the probability determination unit includes: The transformation subunit is used to perform discrete Fourier transformation on the zero-one sequence of any market fluctuation type to obtain frequency components of different frequencies; A selection subunit is used to select characteristic frequency components according to the amplitudes of frequency components of different frequencies; A period determination subunit, used to determine the fluctuation period according to the frequency of the selected characteristic frequency component; A cycle application subunit, used for determining the probability of generating the market fluctuation type based on the fluctuation cycle; The process of determining the probability is: ; In the formula, Indicates the probability of a certain type of market fluctuation, is the preset correction factor, represents the number of selected characteristic frequency components, It means that after sorting the characteristic frequency components in descending order based on the amplitude, The amplitude of the characteristic frequency component, Indicates The period corresponding to the characteristic frequency components; Indicates the time when this type of market fluctuation last occurred. Indicates how long it has been since this type of market volatility last occurred. Updated every time a market volatility type occurs, Returns to zero each time a market volatility type occurs.

[0042] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A big data-based innovation and entrepreneurship simulation method, characterized in that: The method comprises: Determine enterprises of different fields and sizes according to a preset ratio, send information query requests to the enterprises, and receive cost input information containing time information and asset information containing time information fed back by the enterprises; Obtaining market fluctuation parameters within a preset time range, and determining the probability of market fluctuations occurring at each moment according to the market fluctuation parameters; the market fluctuation parameters include market fluctuation types and occurrence moments; An information simulation port is opened, and simulation information input by a user is received based on the information simulation port, and the simulation information is compared with the cost input information containing time information, so as to match similar enterprises, and the asset information containing time information of the similar enterprises is read as a simulation result; Market fluctuations are randomly introduced based on the probability of market fluctuations at each moment, and the simulation results are corrected.

2. The innovation and entrepreneurship simulation method based on big data according to claim 1 is characterized in that: The steps of determining enterprises of different fields and sizes according to a preset ratio, sending information query requests to the enterprises, and receiving cost input information containing time information and asset information containing time information fed back by the enterprises include: Obtaining professional information of the simulated group, and determining the enterprise field according to the professional information; receiving two geometric series set by the management party as the enterprise scale series and the enterprise quantity series respectively; the first term, the last term and the common ratio of the geometric series are preset values; For any data in the enterprise scale series, query the enterprise based on the data, send an information query request to the enterprise, and receive the cost input information containing time information and asset information containing time information fed back by the enterprise; Record the number of enterprises that provide feedback information, and when the number meets the enterprise number sequence, stop the information acquisition process; Among them, the cost investment information is an array set, each array in the array set contains a time tag, each element in each array corresponds to the investment type one by one, and the element value represents the investment amount; the asset information containing time information is also an array set, each array in the array set contains a time tag, each element in each array corresponds to the asset type one by one, and the element value represents the asset amount.

3. The innovation and entrepreneurship simulation method based on big data according to claim 1 is characterized in that: The step of obtaining the market volatility parameters within the preset time range and determining the probability of market volatility at each moment according to the market volatility parameters comprises: The time frame for receiving the management input; Get the moment of occurrence of each market movement type within the time range; Count the occurrence time of each market fluctuation type in chronological order and create a zero-one sequence; one means it occurs, and zero means it does not occur; The probability of each type of market fluctuation occurring at each moment is determined based on the zero-one sequence.

4. The innovation and entrepreneurship simulation method based on big data according to claim 3 is characterized in that: The step of determining the probability of each type of market fluctuation occurring at each moment according to the zero-one sequence comprises: For any zero-one sequence of market fluctuation type, perform discrete Fourier transform to obtain frequency components of different frequencies; Selecting characteristic frequency components according to the amplitudes of frequency components of different frequencies; Determine the fluctuation period according to the frequency of the selected characteristic frequency component; Determine the probability of generating the market fluctuation type based on the fluctuation period; The process of determining the probability is: ; In the formula, Indicates the probability of a certain type of market fluctuation, is the preset correction factor, represents the number of selected characteristic frequency components, It means that after sorting the characteristic frequency components in descending order based on the amplitude, The amplitude of the characteristic frequency component, Indicates The period corresponding to the characteristic frequency components; Indicates the time when this type of market fluctuation last occurred. Indicates how long it has been since this type of market volatility last occurred. Updated every time a market volatility type occurs, Returns to zero each time a market movement type occurs.

5. The innovation and entrepreneurship simulation method based on big data according to claim 4 is characterized in that: The step of opening the information simulation port, receiving simulation information input by the user based on the information simulation port, comparing the simulation information with the cost input information containing time information, matching similar enterprises, and reading the asset information containing time information of the similar enterprises as the simulation result includes: Opening an information simulation port, and receiving cost input information input by a user based on the information simulation port; Compare the cost input information entered by the user with the cost input information of the enterprise containing time information to match similar enterprises; Read asset information containing time information of similar enterprises, determine the asset information at the current moment, and feedback to the user.

6. The innovation and entrepreneurship simulation method based on big data according to claim 5 is characterized in that: The step of randomly introducing market fluctuations based on the probability of market fluctuations at each moment and correcting the simulation results comprises: Query the probability of various market fluctuation types occurring at each moment; Randomly introduce market fluctuation types based on the probability, input the market fluctuation types into a preset impact prediction model, and output a correction coefficient; Correcting the asset information at the current moment according to the correction coefficient; Among them, the impact prediction model is a mapping of market volatility types and the degree of impact on different assets.

7. An innovation and entrepreneurship simulation system based on big data, characterized in that: The system comprises: An information pre-acquisition module is used to determine enterprises of different fields and sizes according to a preset ratio, send information query requests to the enterprises, and receive cost input information containing time information and asset information containing time information fed back by the enterprises; A fluctuation statistics module is used to obtain market fluctuation parameters within a preset time range, and determine the probability of market fluctuations occurring at each moment according to the market fluctuation parameters; the market fluctuation parameters include market fluctuation types and occurrence times; An enterprise simulation module is used to open an information simulation port, receive simulation information input by a user based on the information simulation port, compare the simulation information with cost input information containing time information, match similar enterprises, and read asset information containing time information of similar enterprises as a simulation result; The volatility introduction module is used to randomly introduce market fluctuations based on the probability of market fluctuations at each moment and to correct the simulation results.

8. The innovation and entrepreneurship simulation system based on big data according to claim 7 is characterized in that: The information pre-acquisition module comprises: A field determination unit, used to obtain professional information of the simulated group and determine the enterprise field according to the professional information; A sequence creation unit is used to receive two geometric sequence set by the management party, which are used as the enterprise scale sequence and the enterprise quantity sequence respectively; the first term, the last term and the common ratio of the geometric sequence are preset values; An information query unit is used to query the enterprise based on any data in the enterprise scale series, send an information query request to the enterprise, and receive cost input information containing time information and asset information containing time information fed back by the enterprise; A process management unit, used to record the number of enterprises that have provided feedback information, and when the number meets the enterprise number series, stop the information acquisition process; Among them, the cost investment information is an array set, each array in the array set contains a time tag, each element in each array corresponds to the investment type one by one, and the element value represents the investment amount; the asset information containing time information is also an array set, each array in the array set contains a time tag, each element in each array corresponds to the asset type one by one, and the element value represents the asset amount.

9. The innovation and entrepreneurship simulation system based on big data according to claim 7 is characterized in that: The fluctuation statistics module includes: A time range receiving unit, used to receive a time range input by a management party; Amplitude acquisition unit, used to obtain the occurrence moment of each market fluctuation type within the time range; The sequence creation unit is used to count the occurrence time of each market fluctuation type according to the time sequence and create a zero-one sequence; one means occurrence, and zero means no occurrence; The probability determination unit is used to determine the probability of each type of market fluctuation occurring at each moment according to the zero-one sequence.

10. The innovation and entrepreneurship simulation system based on big data according to claim 9 is characterized in that: The probability determination unit comprises: The transformation subunit is used to perform discrete Fourier transformation on the zero-one sequence of any market fluctuation type to obtain frequency components of different frequencies; A selection subunit is used to select characteristic frequency components according to the amplitudes of frequency components of different frequencies; A period determination subunit, used to determine the fluctuation period according to the frequency of the selected characteristic frequency component; A cycle application subunit, used for determining the probability of generating the market fluctuation type based on the fluctuation cycle; The process of determining the probability is: ; In the formula, Indicates the probability of a certain type of market fluctuation, is the preset correction factor, represents the number of selected characteristic frequency components, It means that after sorting the characteristic frequency components in descending order based on the amplitude, The amplitude of the characteristic frequency component, Indicates The period corresponding to the characteristic frequency components; Indicates the time when this type of market fluctuation last occurred. Indicates how long it has been since this type of market volatility last occurred. Updated every time a market volatility type occurs, Returns to zero each time a market movement type occurs.

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