Simulation processing method and device applied to virtual goods and electronic equipment

Abstract

Embodiments of the present disclosure disclose a simulation processing method and device applied to virtual goods and an electronic device. A specific embodiment of the method comprises: in response to reaching an initial time, performing scene initialization on a virtual goods processing scene; in response to the scene initialization being completed, determining an order demand information set corresponding to each virtual object in a virtual object set; for each virtual object in the virtual object set, performing the following processing steps: determining an order placement probability according to the order demand information set corresponding to the virtual object; generating a virtual goods order according to the order placement probability, the object type corresponding to the virtual object, and the order demand information set corresponding to the virtual object; performing order matching on the virtual goods order; and in response to the matching being successful, performing scene updating on the virtual goods processing scene according to the virtual goods order. The embodiment realizes simulation processing of virtual goods.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular, to a simulation processing method and device applied to virtual goods and an electronic device. BACKGROUND

[0002] Virtual goods (for example, stocks, etc.) are affected by various factors during processing, and negative factors often have a negative impact on the processing of virtual goods. How to ensure the normal processing of virtual goods has become a problem to be solved. At present, when processing virtual goods, the commonly used way is: for example, by applying constraints (for example, rule constraints, etc.) to virtual goods, and adjusting the constraints according to the actual processing result.

[0003] However, the inventors have found that when the above-mentioned way is used, the following technical problem one often exists:

[0004] The way of adjusting later in combination with the actual processing result has strong lag, and when the constraint is wrong, it may further cause improper processing of virtual goods, thereby affecting the circulation of virtual goods.

[0005] When the simulation way is used to simulate the processing of virtual goods, the following technical problem two often exists:

[0006] The actual circulation process of virtual goods also has an impact on virtual goods, and the lack of effective simulation of the circulation process often leads to distorted simulation results.

[0007] The above information disclosed in the background section is only used to enhance the understanding of the background of the present inventive concept, and therefore, it can contain information that does not form the prior art known to those of ordinary skill in the art. SUMMARY

[0008] The summary section of the present disclosure is used to introduce the concepts in a brief form, which will be described in detail in the specific embodiments section. The summary section of the present disclosure is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0009] Some embodiments of the present disclosure propose a simulation processing method and device applied to virtual goods and an electronic device to solve one or more of the technical problems mentioned in the background section.

[0010] In a first aspect, some embodiments of the present disclosure provide a simulation processing method applied to virtual goods, comprising: in response to reaching an initial time, performing scene initialization on a virtual goods processing scene; in response to the scene initialization being completed, determining an order demand information set corresponding to each virtual object in a virtual object set, wherein the virtual object is to generate a virtual goods order for the virtual goods through the virtual goods processing scene simulation, the order demand information set represents the order demand of the virtual object for at least one virtual good, and the object type corresponding to the virtual object includes a first object type, a second object type, a third object type, a fourth object type, and a fifth object type, wherein the first object type represents that the virtual object randomly places an order for the virtual goods, the second object type represents that the virtual object places an order for the virtual goods based on a current value attribute of the virtual goods, the third object type represents that the virtual object places an order for the virtual goods based on a lag value attribute of the virtual goods, the fourth object type represents that the virtual object places an order for the virtual goods based on a historical value attribute trend of the virtual goods, and the fifth object type represents that the virtual object places an order for the virtual goods based on the historical value attribute trend of the virtual goods and a scene state of the virtual goods scene; for each virtual object in the virtual object set, the following processing steps are performed: determining an order placement probability according to the order demand information set corresponding to the virtual object, wherein the order placement probability represents the probability of the virtual object placing an order for the virtual goods in the virtual goods processing scene; generating a virtual goods order according to the order placement probability, the object type corresponding to the virtual object, and the corresponding order demand information set, wherein the virtual goods order includes a virtual order state and virtual order description information; performing order matching on the virtual goods order; and in response to the matching being successful, performing scene updating on the virtual goods processing scene according to the virtual goods order.

[0011] In a second aspect, some embodiments of the present disclosure provide a simulation processing device applied to a virtual item, the device comprising: a scene initialization unit configured to, in response to reaching an initial time, perform scene initialization on a virtual item processing scene; a determination unit configured to, in response to the scene initialization being completed, determine an order demand information set corresponding to each virtual object in a virtual object set, wherein the virtual object is to generate a virtual item order for a virtual item through simulation of the virtual item processing scene, the order demand information set represents order demand of the virtual object for at least one virtual item, and the object type corresponding to the virtual object comprises a first object type, a second object type, a third object type, a fourth object type, and a fifth object type, wherein the first object type represents that the virtual object randomly places an order for a virtual item, the second object type represents that the virtual object places an order for a virtual item based on a current value attribute of the virtual item, the third object type represents that the virtual object places an order for a virtual item based on a lag value attribute of the virtual item, the fourth object type represents that the virtual object places an order for a virtual item based on a historical value attribute trend of the virtual item, and the fifth object type represents that the virtual object places an order for a virtual item based on a historical value attribute trend of the virtual item and a scene state of the virtual item scene; and an execution unit configured to, for each virtual object in the virtual object set, perform the following processing steps: determining an order placement probability based on the order demand information set corresponding to the virtual object, wherein the order placement probability represents a probability that the virtual object places an order for a virtual item in the virtual item processing scene; generating a virtual item order based on the order placement probability, the object type corresponding to the virtual object, and the order demand information set corresponding to the virtual object, wherein the virtual item order comprises a virtual order state and virtual order description information; performing order matching on the virtual item order; and in response to successful matching, performing scene updating on the virtual item processing scene based on the virtual item order.

[0012] In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the implementations of the first aspect.

[0013] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any of the implementations of the first aspect is implemented.

[0014] The above various embodiments of the present disclosure have the following beneficial effects: through the application of the simulation processing method for virtual objects of some embodiments of the present disclosure, the hysteresis problem is solved, and the influence on the circulation of virtual objects caused by improper processing is avoided to some extent. For example, when the virtual object is a stock, in order to ensure the normal transaction circulation of the stock, it is often necessary to impose corresponding constraint rules, however, the effect of imposing the constraint rules often needs to be verified after being imposed on the stock and affecting its circulation. When an error constraint occurs, it may cause improper processing of the stock, thereby affecting the stock circulation, and even stock price fluctuations. Based on this, the simulation processing method for virtual objects of some embodiments of the present disclosure first responds to the arrival of the initial time to initialize the virtual object processing scene. The virtual object processing scene is constructed to build a simulated processing scene. Secondly, in response to the completion of the scene initialization, the order demand information group corresponding to each virtual object in the virtual object set is determined, wherein the virtual object is to generate a virtual object order for the virtual object through the above virtual object processing scene simulation, the order demand information group represents the order demand of the virtual object for at least one virtual object, and the object type corresponding to the virtual object includes a first object type, a second object type, a third object type, a fourth object type and a fifth object type, wherein the first object type represents that the virtual object randomly places an order for the virtual object, the second object type represents that the virtual object places an order for the virtual object based on the current value attribute of the virtual object, the third object type represents that the virtual object places an order for the virtual object based on the lag value attribute of the virtual object, the fourth object type represents that the virtual object places an order for the virtual object based on the historical value attribute trend of the virtual object, and the fifth object type represents that the virtual object places an order for the virtual object based on the historical value attribute trend of the virtual object and the scene state of the virtual object scene. In this way, the order demand (e.g., transaction demand for stocks) corresponding to different virtual objects is simulated in units of virtual objects. Then, for each virtual object in the virtual object set, the following processing steps are performed: first, according to the order demand information group corresponding to the virtual object, determine the order placement probability, wherein the order placement probability represents the probability of the virtual object placing an order for the virtual object in the virtual object processing scene. The order placement probability is generated to control the virtual object to place an order for the virtual object in the virtual object processing scene. Second, according to the order placement probability, the object type corresponding to the virtual object and the corresponding order demand information group, generate a virtual object order, wherein the virtual object order includes a virtual order state and virtual order description information. In this way, the virtual object order matched with the order demand is generated. Third, the virtual object order is matched. In this way, the effective execution of the virtual order is ensured. Finally, in response to the successful matching, the virtual object processing scene is updated according to the virtual object order.Real-time update of the virtual item processing scene is realized. The simulation processing of the virtual item is realized, the hysteresis problem is solved, and the influence on the circulation of the virtual item caused by improper processing is avoided to some extent. BRIEF DESCRIPTION OF DRAWINGS

[0015] The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent upon understanding the following detailed description, taken in conjunction with the accompanying drawings. Throughout the drawings, similar or same reference numerals are used to denote similar or same elements. It should be understood that the drawings are schematic and elements and features are not necessarily to scale.

[0016] Figure 1 is a flowchart of some embodiments of a simulation processing method applied to a virtual item according to the present disclosure;

[0017] Figure 2 is a structural schematic diagram of some embodiments of a simulation processing apparatus applied to a virtual item according to the present disclosure;

[0018] Figure 3 is a structural schematic diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes, and are not intended to limit the scope of protection of the present disclosure.

[0020] In addition, it should be further noted that only parts related to the invention are shown in the drawings for ease of description. The embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0021] It should be noted that the terms "first", "second", etc. mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0022] It should be noted that the adjectives "one", "multiple" mentioned in the present disclosure are illustrative and not limiting, and those skilled in the art should understand that unless otherwise explicitly stated in the context, it should be understood as "one or more".

[0023] Names of messages or information exchanged between multiple devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0024] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0025] With reference to Figure 1 Fig. 100 shows a flow 100 of some embodiments of the simulation processing method applied to virtual items according to the present disclosure. The simulation processing method applied to virtual items includes the following steps:

[0026] In step 101, in response to reaching an initial time, a scene initialization is performed on a virtual item processing scene.

[0027] In some embodiments, the execution subject (for example, a computing device) of the simulation processing method applied to virtual items can perform a scene initialization on a virtual item processing scene in response to reaching an initial time. The initial time can be a preset time for performing a scene initialization on a virtual item processing scene. As an example, for the processing of virtual items (for example, stock trading), there are often opening hours and closing hours in the actual process. In order to ensure the authenticity of the simulation, the initial time is set to correspond to the opening hours to control the start of the simulation. The virtual item processing scene is a simulation scene for a virtual object to process virtual items. In practice, the virtual item processing scene can include a virtual object pool, an order matching module, and an information recording module. The virtual object pool is used to generate virtual objects to perform simulation processing on virtual items by taking virtual objects as the subject. The order matching module is used to match virtual item orders to simulate the processing (for example, order transaction) of the orders. The information recording module is used to record simulation data involved in the virtual item processing scene. The simulation data includes but is not limited to overall setting data, object information, virtual scene description information, an order book, daily data records, and processing records of virtual items. The overall setting data is the basic scene data of the virtual item processing scene. The object information is attribute information used to characterize virtual objects. The virtual scene description information is used to describe the scene state of the virtual item processing scene and the index data in the simulation process. The order book is used to record the order status of virtual item orders. The daily data records are used to record the relevant records of each virtual item at a daily granularity. The processing records of virtual items are used to record the generated virtual item orders and order processing records (for example, transaction records). In practice, the above execution subject can initialize the virtual object pool and the information recording module. For example, the above execution subject can clear the order status of the virtual item orders recorded in the order book recorded by the information recording module.

[0028] In practice, the variables and variable meanings included in the overall setting data can be shown in Table 1 as follows:

[0029] Table 1

[0030]

[0031]

[0032] In practice, the variables and variable meanings included in the object information can be shown in Table 2 as follows:

[0033] Table 2

[0034]

[0035] Specifically, "II.information" is used to record the object type of the virtual object, the actual cash holding amount of the virtual object, the available cash amount of the virtual object, and the order aggressiveness. For example, when a transaction for a virtual item is formed, the actual cash holding amount decreases. When a limit order for a virtual item is formed, the available cash amount decreases.

[0036] Specifically, "II.price" is used to record the expected value attribute (e.g., expected price) of the virtual object for the virtual item.

[0037] Specifically, "II.position" is used to record the actual position of each virtual object for each virtual item.

[0038] Specifically, "II.pending_orders" is used to record the limit order situation of each virtual object for each virtual item.

[0039] Specifically, "II.pending_orders" is used to record the limit order situation of the virtual object numbered as account for the virtual item numbered as code. For example, "II_not_ordering_orders" is used to record the order number, the limit order price, the limit order direction, the limit order quantity, the limit order time, and the limit order period. The limit order direction includes: limit order and no limit order. Limit order represents buy, and no limit order represents sell.

[0040] Specifically, "II_not_ordering_orders" is used to record the order demand amount of the virtual object for the day. For example, as the order is placed, the order demand amount decreases.

[0041] Specifically, "II.enter" represents whether the virtual object is allowed to place an order. For example, "II.enter" adopts a boolean data type.

[0042] In practice, the variables and their meanings included in the virtual scene description information can be seen in Table 3 below:

[0043] Table 3

[0044]

[0045] Specifically, "Mi.information" is used to record changes in the virtual scene. For example, "Mi.information" is used to record: the current highest price, the intraday highest price, the intraday lowest price, the price under the first delayed matching state, the price under the last delayed matching state, the limit on price increase, and the limit on price decrease.

[0046] Specifically, "Mi.information.minute" is used to record information generated during the trading of virtual items at each moment, including a first information record and a second information record. The first information record uses a two-dimensional dataframe structure with a size of num_period × 10. For example, the first information record records: the transaction price of the first transaction at the current moment, the transaction price of the last transaction at the current moment, the highest transaction price at the current moment, the lowest transaction price at the current moment, the average transaction price at the current moment, the market price at the current moment, the total transaction amount at the current moment, the total number of virtual items at the current moment, the number of orders in the completed transaction state at the current moment, the total number of orders generated at the current moment, and the base price of the virtual item at the current moment. The second information record uses a two-dimensional dataframe structure with a size of num_period × 20. For example, the second information record is used to record: the fifth highest buy price, the order volume corresponding to the fifth highest buy price, the fourth highest buy price, the order volume corresponding to the fourth highest buy price, the third highest buy price, the order volume corresponding to the third highest buy price, the second highest buy price, the order volume corresponding to the second highest buy price, the first highest buy price, the order volume corresponding to the first highest buy price, the fifth lowest sell price, the order volume corresponding to the fifth lowest sell price, the fourth lowest sell price, the order volume corresponding to the fourth lowest sell price, the third lowest sell price, the order volume corresponding to the third lowest sell price, the second lowest sell price, the order volume corresponding to the second lowest sell price, the first lowest sell price, and the order volume corresponding to the first lowest sell price.

[0047] Specifically, "Mi.information.values" is used to record the basic value of virtual items at the start of each day.

[0048] In practice, the variables included in the order book and their meanings can be seen in Table 4 below:

[0049] Table 4

[0050]

[0051] Specifically, the "OB" is used to record the order state of the virtual item order in the virtual item processing scene, including: buy order state and sell order state. The buy order state adopts a two-dimensional np.array data structure, and the data structure size is: buy_order_n x 9. The sell order state adopts a two-dimensional np.array data structure, and the data structure size is: buy_order_n x 9. The buy order state and the sell order state both include: transaction type, object identifier corresponding to the virtual object, order quantity, order price, order time, object type of the virtual object, market price, base value, and order number. Among them, the transaction type is used to distinguish the real-time matching state and the delayed matching state. The order quantity represents the quantity of the virtual item. The order price represents the virtual item price at the time of placing the order. The order time represents the time when the order is generated. The market price represents the value attribute of the virtual item corresponding to the order at the time of generating the order. The base value represents the base value of the virtual item corresponding to the order at the time of generating the order. The order number is the unique identifier of the order.

[0052] In practice, the daily data record is used to record: opening price, closing price, highest price, lowest price, opening base value and closing base value. Among them, the opening price represents the first price in the delayed matching state. The closing price represents the last price in the delayed matching state. The highest price represents the highest price in the day, and the lowest price represents the lowest price in the day. The opening base value represents the corresponding base value when the opening price is generated. The closing base value represents the corresponding base value when the closing price is generated. Specifically, the daily data record can be used to record the daily data of day, or the daily data of the virtual item numbered as codes.

[0053] In practice, the processing record of the virtual item includes variables and variable meanings, which can be shown in Table 5 as follows:

[0054] Table 5

[0055]

[0056] Specifically, "OB" is used to record high-frequency data in the virtual item processing scene. "TR" is used to record information records generated by transactions, including: order number, transaction number, order record (Tr.loc1), transaction record (Tr.loc2). Among them, the order number and the transaction number are both integer data structures. For example, "Tr.loc1" is used to record the order of the virtual item with the code when the transaction is completed, including: transaction price, transaction time, transaction period, transaction number, buy order price, buy order market price, buy order base value, object identifier of the virtual object of the buy order, buy order time, buy order period, object type of the virtual object of the buy order, buy order number, buy order price, buy order market price, buy order base price, sell order price, sell order market price, sell order base price, object identifier of the virtual object of the sell order, sell order time, sell order period, object type of the virtual object of the sell order, sell order number. "Tr.loc2" is used to record the order of the virtual item with the code, including: transaction type, object identifier of the virtual object, transaction direction, order quantity, order price, order time, object type of the virtual object, market price, base value, order number. Among them, the transaction direction includes: buy transaction and sell transaction.

[0057] It should be noted that the above computing device can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster composed of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is software, it can be installed in the above-mentioned hardware devices. It can be implemented as, for example, multiple software or software modules for providing distributed services, or as a single software or software module. No specific limitation is made here. It should be understood that the number of computing devices can have any number as needed for implementation.

[0058] Step 102, in response to the completion of the scene initialization, determining an order demand information group corresponding to each virtual object in the virtual object set.

[0059] In some embodiments, in response to the completion of the scene initialization, the execution subject can determine an order demand information set corresponding to each virtual object in the virtual object set. The virtual object is to generate a virtual commodity order for a virtual commodity through the virtual commodity processing scene simulation. For example, the execution subject can determine the number of virtual objects of the same object type through the "investors" parameter in the overall setting data, and determine the number of virtual objects in the virtual object set through the "num_investors" parameter in the overall setting data. The order demand information set represents the order demand of the virtual object for at least one virtual commodity. For example, the order demand information can include a virtual commodity demand quantity and a commodity identifier corresponding to the virtual commodity. The object type corresponding to the virtual object includes a first object type, a second object type, a third object type, a fourth object type, and a fifth object type. The first object type represents that the virtual object randomly places an order for a virtual commodity. The second object type represents that the virtual object places an order for a virtual commodity based on a current value attribute of the virtual commodity. For example, the current value attribute can be a current price. The third object type represents that the virtual object places an order for a virtual commodity based on a lag value attribute of the virtual commodity. For example, the lag value attribute can be a lag price. For example, the current time is T, the corresponding current price is T+1, and the corresponding lag price is T+1. The fourth object type represents that the virtual object places an order for a virtual commodity based on a historical value attribute trend of the virtual commodity. For example, the historical value attribute trend is the change trend of the historical price. The fifth object type represents that the virtual object places an order for a virtual commodity based on the historical value attribute trend of the virtual commodity and the scene state of the virtual commodity scene.

[0060] Optionally, the order demand information in the order demand information set includes an expected value attribute and a daily granularity demand quantity. The expected value attribute represents the expected commodity price of the virtual object for the virtual commodity. The daily granularity demand quantity represents the demand quantity of the virtual object for the virtual commodity in a day as a time granularity.

[0061] In some optional implementations of some embodiments, the determination of the order demand information set corresponding to each virtual object in the virtual object set can include the following steps:

[0062] First, for each virtual commodity in the virtual commodity set, the following baseline value attribute determination step is performed:

[0063] First sub-step, in response to the object type corresponding to the virtual object being the first object type, the baseline value attribute corresponding to the virtual commodity is determined based on the first current value attribute corresponding to the virtual commodity.

[0064] The first current value attribute is a current market price of the virtual item. For example, the execution subject can take the first current value attribute as the reference value attribute corresponding to the virtual item. For another example, the execution subject can increase or decrease the value of the first current value attribute to obtain the reference value attribute corresponding to the virtual item.

[0065] The second sub-step is to determine the reference value attribute corresponding to the virtual item based on a second current value attribute corresponding to the virtual item, in response to the object type corresponding to the virtual object being a second object type.

[0066] The second current value attribute is a value attribute corresponding to the virtual item determined by a fundamental analysis. For example, the execution subject can take the second current value attribute as the reference value attribute corresponding to the virtual item. For another example, the execution subject can increase or decrease the value of the second current value attribute to obtain the reference value attribute corresponding to the virtual item.

[0067] The third sub-step is to determine the reference value attribute corresponding to the virtual item based on a lag value attribute corresponding to the virtual item, in response to the object type corresponding to the virtual object being a third object type.

[0068] The lag value attribute is a value attribute corresponding to the virtual item at a lag time. For example, the current time is T, the current value attribute corresponds to the current time, and the next time is T+1, the lag value attribute corresponds to the next time. In practice, the lag value attribute can be a value attribute corresponding to the virtual item at a lag of one day. For example, the execution subject can take the lag value attribute as the reference value attribute corresponding to the virtual item. For another example, the execution subject can increase or decrease the value of the lag value attribute to obtain the reference value attribute corresponding to the virtual item.

[0069] The fourth sub-step is to determine the reference value attribute corresponding to the virtual item based on a first historical value attribute trend corresponding to the virtual item, in response to the object type corresponding to the virtual object being a fourth object type.

[0070] The first historical value attribute trend is a historical value attribute trend of the virtual item in a historical time period. Specifically, the first historical value attribute trend represents a trend of a value attribute corresponding to the virtual item in a historical time period. In practice, the execution subject can obtain the reference value attribute corresponding to the virtual item based on the continuous first historical value attribute trend by time series prediction.

[0071] In response to the object type corresponding to the virtual object being the fifth object type, a second historical value attribute trend corresponding to the virtual item is determined as the reference value attribute corresponding to the virtual item.

[0072] The second historical value attribute is a historical value attribute trend of the virtual item at at least two historical time points. Specifically, the historical time points can be discrete time points within a historical time. The second historical value attribute can be obtained by time series prediction based on the discrete second historical value attribute trend, and the reference value attribute corresponding to the virtual item.

[0073] In the sixth sub-step, noise is added to the reference value attribute corresponding to the virtual item to obtain a noise-added reference value attribute as an expected value attribute included in order demand information in the order demand information group corresponding to the virtual item.

[0074] In practice, the execution subject can obtain a noise-added reference value attribute by adding a random noise value within a noise range to the reference value attribute as an expected value attribute included in order demand information in the order demand information group corresponding to the virtual item. Specifically, the expected value attribute corresponds to an "initial_price" parameter.

[0075] In the seventh sub-step, a daily granularity demand amount included in order demand information corresponding to the noise-added reference value in the order demand information group corresponding to the virtual item is generated according to the noise-added reference value attribute and the order placement aggressiveness corresponding to the virtual object.

[0076] In practice, when the difference between the benchmark value attribute (expected value attribute) after adding noise and the market price corresponding to the virtual object is less than or equal to a preset threshold, the daily granularity demand is 0. The preset threshold is the cost generated by the transaction, such as stamp duty. When the difference between the benchmark value attribute (expected value attribute) after adding noise and the market price corresponding to the virtual object is greater than the preset threshold, the difference between the benchmark value attribute (expected value attribute) after adding noise and the market price corresponding to the virtual object is positively correlated with the daily granularity demand, that is, the greater the difference between the benchmark value attribute (expected value attribute) after adding noise and the market price corresponding to the virtual object, the higher the daily granularity demand. At the same time, the order placing aggressiveness is used as a coefficient for generating the daily granularity demand, so as to obtain the daily granularity demand. For example, the generation mode of the daily granularity demand can be functionized as: daily granularity demand = F(noise-added benchmark value) x order placing aggressiveness. Alternatively, the initial position of the virtual object and the initial capital can also be considered as influence parameters corresponding to the daily granularity demand, so the generation mode of the daily granularity demand can be further functionized as: daily granularity demand = F(noise-added benchmark value, initial position, initial capital) x order placing aggressiveness.

[0077] The above "in some optional implementations of some embodiments" is an invention point of the present disclosure, which solves the second technical problem mentioned in the background, that is, "the actual virtual object transfer process also has an impact on the virtual object, and the lack of effective simulation of the transfer process often leads to distorted simulation results". Based on this, the present disclosure constructs virtual objects of the first object type, the second object type, the third object type, the fourth object type and the fifth object type from the object feature angle of the virtual object, to simulate the object characteristics of different actual transaction objects. This enriches the diversity of simulation. At the same time, different transaction characteristics correspond to virtual objects of different object types, so the actual situation of the virtual object transfer can be effectively simulated, and the simulation of the object transfer is ensured, so the effectiveness of the simulation result is ensured.

[0078] Step 103, for each virtual object in the virtual object set, the following processing steps are performed:

[0079] Step 1031, determine the order placing probability according to the order demand information group corresponding to the virtual object.

[0080] In some embodiments, the above execution subject can determine the order placing probability according to the order demand information group corresponding to the virtual object. The order placing probability represents the probability of placing an order for a virtual object in the virtual object processing scenario. In practice, the daily granularity demand included in the order demand information is positively correlated with the order placing probability, that is, the greater the daily granularity demand, the greater the order placing probability.

[0081] In some optional implementations of some embodiments, the execution subject determines the order placement probability according to the order demand information set corresponding to the virtual object, which can include the following steps:

[0082] First, determine the remaining order demand quantity according to the order demand information set corresponding to the virtual object.

[0083] The remaining order demand quantity represents the total order demand quantity that has not been placed by the order demand information set, and specifically, the remaining order demand quantity = daily granularity demand quantity - order demand quantity that has been placed.

[0084] Second, determine the order placement probability according to the remaining order demand quantity.

[0085] The order placement probability and the remaining order demand quantity are positively correlated with the Poisson distribution. Specifically, P(X = remaining order demand quantity) = probability mass function of Poisson distribution. P(X = remaining order demand quantity) represents the order placement probability. In addition, it can also be denoted as X ~ Poisson(λ), which represents that X follows the Poisson distribution with parameter λ, where the value of λ is different, the function curve of the probability mass function of the Poisson distribution is different. For example, when λ is 10, the function curve of the probability mass function is approximately the function curve of the normal distribution (X = 10 is the boundary, when 0 ≤ X ≤ 10, X and P are positively correlated, when X > 10, X and P are negatively correlated). Therefore, the order placement probability and the remaining order demand quantity are positively correlated with the Poisson distribution.

[0086] Step 1032, generate a virtual item order according to the order placement probability, the object type corresponding to the virtual object, and the corresponding order demand information set.

[0087] In some embodiments, the execution subject can generate a virtual item order according to the order placement probability, the object type corresponding to the virtual object, and the corresponding order demand information group. The virtual item order includes a virtual order state and virtual order description information. The virtual order state represents the order completion state of the virtual order. For example, the virtual order state includes an order completion state and an order non-completion state. The virtual order description information represents the order description of the virtual order. The virtual item order can be an order that is not written into the order book. In practice, the order state of the virtual item is initially in the order non-completion state. The virtual order description information can include an order number, an object type of a virtual object, a limit order price, a limit order quantity, a limit order price, a limit order time, a market price, and a base price. Specifically, the execution subject can use the order placement probability as the probability of generating a virtual item order corresponding to the virtual object. The greater the order placement probability, the stronger the demand for placing an order for the virtual object, and therefore, it is more likely to generate a virtual item order. The execution subject can combine the object type corresponding to the virtual object, the corresponding order demand information group, and the current limit order price, limit order quantity, limit order price, limit order time, market price, and base price corresponding to the order to obtain the virtual item order.

[0088] In step 1033, the virtual item order is order matched.

[0089] In some embodiments, the execution subject can order match the virtual item order. In practice, the virtual item order is an order that is not updated to the order book, and an actual transaction is generated according to the virtual item order, that is, no impact is generated on the virtual item. Therefore, the execution subject can update to the order book in order according to the generation order of the virtual item order, so as to realize order matching of the virtual item order (generate an actual transaction for the virtual item according to the virtual item order).

[0090] In some optional implementations of some embodiments, the execution subject can order match the virtual item order, which can include the following steps:

[0091] First, determine the order matching state.

[0092] The order matching state includes a real-time matching state and a delayed matching state. The real-time matching state represents matching orders according to the order generation sequence. The delayed matching state represents matching orders in a delayed manner. Specifically, the real-time matching state represents generating simulated transactions for virtual goods according to the order generation sequence. The delayed matching state represents generating simulated transactions for virtual goods in a delayed manner. In practice, the execution subject can determine the order matching state according to a parameter value of a “peroid” parameter included in the overall setting data. The parameter value of the “peroid” parameter includes the real-time matching state and the delayed matching state.

[0093] In response to the order matching state being the real-time matching state, the order book is updated in real time according to the virtual good order to realize order matching.

[0094] In practice, when the order matching state is the real-time matching state, the execution subject can update the order book according to the virtual good order according to the order generation sequence, and generate simulated transactions for virtual goods corresponding to the virtual good order to realize order matching. The order book is used to record virtual good orders that are successfully matched in the virtual good processing scenario.

[0095] In response to the order matching state being the delayed matching state, the following delayed order matching steps are performed:

[0096] In the first sub-step, the virtual order matching is performed on the virtual good order to generate a virtual matching record.

[0097] As an example, the execution subject can generate a virtual transaction record for the virtual good corresponding to the virtual good order as the virtual matching record.

[0098] In the second sub-step, in response to the order matching state being updated from the delayed matching state to the real-time matching state, the order book is updated according to the virtual matching record to realize order matching.

[0099] In practice, the execution subject can update the order book according to the virtual matching record, and generate simulated transactions for virtual goods corresponding to the virtual good order to realize order matching.

[0100] In response to the matching being successful, the virtual good processing scenario is updated according to the virtual good order.

[0101] In some embodiments, in response to the success of the matching, a scene update is performed on the virtual item processing scene according to the virtual item order. In practice, the above execution subject can update the simulation data recorded by the information recording module corresponding to the virtual item processing scene according to the virtual item order.

[0102] In some optional implementations of some embodiments, after the above-mentioned scene update of the virtual item processing scene according to the virtual item order in response to the success of the matching, the method further includes:

[0103] In response to the success of the matching, an order demand update is performed on the order demand information group corresponding to the virtual object according to the virtual item order information.

[0104] Specifically, when the matching is successful, the execution subject updates the daily granularity demand quantity according to the number of orders included in the virtual item order to obtain an updated daily granularity demand quantity, so as to realize the order demand update of the order demand information group. Specifically, the updated daily granularity demand quantity = daily granularity demand quantity - number of orders.

[0105] In some optional implementations of some embodiments, the method further includes:

[0106] In response to the fact that the virtual order state included in the virtual item order in the obtained virtual item order set is all in the order completion state, and the end time is reached, the simulation data generated between the initial time and the end time is stored.

[0107] The time interval between the initial time and the end time is a daily granularity time interval. For example, the time interval between the initial time and the end time can be 1 day. The simulation data includes but is not limited to the simulation data recorded by the information recording module, the order matching record corresponding to the order matching module, and the object information of the virtual object generated by the virtual object pool.

[0108] The above various embodiments of the present disclosure have the following beneficial effects: through the application of the simulation processing method for virtual objects of some embodiments of the present disclosure, the hysteresis problem is solved, and the influence on the circulation of virtual objects caused by improper processing is avoided to some extent. For example, when the virtual object is a stock, in order to ensure the normal transaction circulation of the stock, it is often necessary to impose corresponding constraint rules, however, the effect of imposing the constraint rules often needs to be verified after being imposed on the stock and affecting its circulation. When an error constraint occurs, it may cause improper processing of the stock, thereby affecting the stock circulation, and even stock price fluctuations. Based on this, the simulation processing method for virtual objects of some embodiments of the present disclosure first responds to the arrival of the initial time to initialize the virtual object processing scene. The virtual object processing scene is constructed to build a simulated processing scene. Secondly, in response to the completion of the scene initialization, the order demand information group corresponding to each virtual object in the virtual object set is determined, wherein the virtual object is to generate a virtual object order for the virtual object through the above virtual object processing scene simulation, the order demand information group represents the order demand of the virtual object for at least one virtual object, and the object type corresponding to the virtual object includes a first object type, a second object type, a third object type, a fourth object type and a fifth object type, wherein the first object type represents that the virtual object randomly places an order for the virtual object, the second object type represents that the virtual object places an order for the virtual object based on the current value attribute of the virtual object, the third object type represents that the virtual object places an order for the virtual object based on the lag value attribute of the virtual object, the fourth object type represents that the virtual object places an order for the virtual object based on the historical value attribute trend of the virtual object, and the fifth object type represents that the virtual object places an order for the virtual object based on the historical value attribute trend of the virtual object and the scene state of the virtual object scene. In this way, the order demand (e.g., transaction demand for stocks) corresponding to different virtual objects is simulated in units of virtual objects. Then, for each virtual object in the virtual object set, the following processing steps are performed: first, according to the order demand information group corresponding to the virtual object, determine the order placement probability, wherein the order placement probability represents the probability of the virtual object placing an order for the virtual object in the virtual object processing scene. The order placement probability is generated to control the virtual object to place an order for the virtual object in the virtual object processing scene. Second, according to the order placement probability, the object type corresponding to the virtual object and the corresponding order demand information group, generate a virtual object order, wherein the virtual object order includes a virtual order state and virtual order description information. In this way, the virtual object order matched with the order demand is generated. Third, the virtual object order is matched. In this way, the effective execution of the virtual order is ensured. Finally, in response to the successful matching, the virtual object processing scene is updated according to the virtual object order.Real-time update of the virtual item processing scene is realized. The simulation processing of the virtual item is realized, the hysteresis problem is solved, and the influence on the circulation of the virtual item caused by improper processing is avoided to some extent.

[0109] Further referring to Figure 2 , as an implementation of the method shown in the above figures, the disclosure provides some embodiments of a simulation processing device applied to virtual items, which correspond to the method embodiments shown in Figure 1 , and the simulation processing device applied to virtual items can be applied to various electronic devices.

[0110] As shown in Figure 2 , the simulation processing device 200 applied to virtual items of some embodiments includes a scene initialization unit 201, a determination unit 202, and an execution unit 203. The scene initialization unit 201 is configured to perform scene initialization on the virtual item processing scene in response to reaching an initial time. The determination unit 202 is configured to determine, in response to the scene initialization being completed, an order demand information set corresponding to each virtual object in a virtual object set, wherein the virtual object is to generate a virtual item order for a virtual item through the above-mentioned simulation of the virtual item processing scene, the order demand information set represents the order demand of the virtual object for at least one virtual item, and the object type corresponding to the virtual object includes a first object type, a second object type, a third object type, a fourth object type, and a fifth object type, wherein the first object type represents that the virtual object randomly places an order for a virtual item, the second object type represents that the virtual object places an order for a virtual item based on the current value attribute of the virtual item, the third object type represents that the virtual object places an order for a virtual item based on the hysteresis value attribute of the virtual item, the fourth object type represents that the virtual object places an order for a virtual item based on the historical value attribute trend of the virtual item, and the fifth object type represents that the virtual object places an order for a virtual item based on the historical value attribute trend of the virtual item and the scene state of the virtual item scene. The execution unit 203 is configured to, for each virtual object in the virtual object set, perform the following processing steps: determining an order placement probability according to the order demand information set corresponding to the virtual object, wherein the order placement probability represents the probability of the virtual object placing an order for a virtual item in the virtual item processing scene; generating a virtual item order according to the order placement probability, the object type corresponding to the virtual object, and the corresponding order demand information set, wherein the virtual item order includes a virtual order state and virtual order description information; order matching the virtual item order; and in response to successful matching, updating the virtual item processing scene according to the virtual item order.

[0111] It can be understood that the units described in the simulation processing apparatus 200 applied to virtual items correspond to the units described in the method with reference to Figure 1 The various steps in the described method correspond. Thus, the operations, features and resulting advantages described above for the method equally apply to the simulation processing apparatus 200 applied to virtual items and the units contained therein, which will not be described again here.

[0112] Reference is made below to Figure 3 which shows a structural schematic diagram of an electronic device (e.g., a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the function and use range of the embodiments of the present disclosure.

[0113] As Figure 3 shown, the electronic device 300 can include a processing apparatus (e.g., a central processor, a graphics processor, etc.) 301, which can perform various appropriate actions and processes according to programs stored in a read-only memory 302 or loaded into a random access memory 303 from a storage device 308. In the random access memory 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing apparatus 301, the read-only memory 302, and the random access memory 303 are connected to each other through a bus 304. An input / output interface 305 is also connected to the bus 304.

[0114] Generally, the following devices can be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 can allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that all the devices shown are not required to be implemented or possessed. More or fewer devices can be alternatively implemented or possessed. Figure 3 Each block shown in the figure can represent a device or, as needed, a plurality of devices.

[0115] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to some embodiments of the present disclosure. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the read only memory 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the methods of some embodiments of the present disclosure are performed.

[0116] It should be noted that the computer readable medium in some embodiments of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination thereof. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any suitable combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. In some embodiments of the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as part of a carrier wave, in which the computer readable program code is carried. Such a propagated data signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. Program code contained in a computer readable medium can be transmitted using any suitable medium, including but not limited to wire, cable, RF (radio frequency), etc., or any suitable combination of the above.

[0117] In some embodiments, the client, server, can communicate using any known or future developed network protocols, such as the HTTP (Hyper Text Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current or future developed networks.

[0118] The computer readable medium described above can be included in the electronic device described above; or can exist separately from the electronic device and not be assembled into the electronic device. The computer readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, cause the electronic device to: in response to reaching an initial time, perform scene initialization on a virtual article processing scene; in response to the scene initialization being completed, determine an order demand information set corresponding to each virtual object in a virtual object set, wherein the virtual object is to generate a virtual article order for a virtual article through simulation of the virtual article processing scene, the order demand information set represents order demand of the virtual object for at least one virtual article, and an object type corresponding to the virtual object includes a first object type, a second object type, a third object type, a fourth object type, and a fifth object type, wherein the first object type represents that the virtual object randomly places an order for a virtual article, the second object type represents that the virtual object places an order for a virtual article based on a current value attribute of the virtual article, the third object type represents that the virtual object places an order for a virtual article based on a lag value attribute of the virtual article, the fourth object type represents that the virtual object places an order for a virtual article based on a historical value attribute trend of the virtual article, and the fifth object type represents that the virtual object places an order for a virtual article based on a historical value attribute trend of the virtual article and a scene state of the virtual article scene; for each virtual object in the virtual object set, the following processing steps are performed: determining an order placing probability according to the order demand information set corresponding to the virtual object, wherein the order placing probability represents a probability that the virtual object places an order for a virtual article in the virtual article processing scene; generating a virtual article order according to the order placing probability, the object type corresponding to the virtual object, and the order demand information set corresponding to the virtual object, wherein the virtual article order includes a virtual order state and virtual order description information; performing order matching on the virtual article order; and in response to successful matching, performing scene updating on the virtual article processing scene according to the virtual article order.

[0119] Computer program code for carrying out operations of some embodiments of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0120] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0121] The units described in some embodiments of the present disclosure can be implemented by hardware, or software, or a combination thereof. The described units can also be arranged in a processor, for example, a processor can be described as including a scene initialization unit, a determination unit, and an execution unit. In some cases, the names of these units do not constitute a limitation on the units themselves, for example, the scene initialization unit can also be described as "a unit that performs scene initialization on a virtual item processing scene in response to reaching an initial time".

[0122] The functionality described herein above can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program- specific Integrated Circuits (ASICs), Program- specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0123] The above description is merely exemplary of the disclosure and the application made use of the principles of the technology. It is to be understood that the application scope of the embodiments of the disclosure is not limited to the specific combinations of technical features described above, and should also cover other technical solutions formed by any combination of the above technical features or equivalent features thereof without departing from the inventive concept. For example, the technical solutions formed by replacing the above features with technical features having similar functions disclosed in the embodiments of the disclosure (but not limited to) with each other.

Claims

1. A simulation processing method applied to a virtual object, comprising: in response to reaching an initial time, performing scene initialization on a virtual object processing scene, the virtual object processing scene comprising: a virtual object pool, an order matching module, and an information recording module, the information recording module being configured to record simulation data involved in the virtual object processing scene, the simulation data comprising: overall setting data, object information, virtual scene description information, an order book, daily data records, and processing records of virtual objects; in response to completion of the scene initialization, determining an order demand information set corresponding to each virtual object in a virtual object set, wherein the virtual object is to generate a virtual object order for a virtual object through simulation of the virtual object processing scene, the order demand information set representing order demand of the virtual object for at least one virtual object, and object types corresponding to the virtual object comprising: a first object type, a second object type, a third object type, a fourth object type, and a fifth object type, wherein the first object type represents that the virtual object randomly places an order for a virtual object, the second object type represents that the virtual object places an order for a virtual object based on a current value attribute of the virtual object, the third object type represents that the virtual object places an order for a virtual object based on a lag value attribute of the virtual object, the fourth object type represents that the virtual object places an order for a virtual object based on a historical value attribute trend of the virtual object, and the fifth object type represents that the virtual object places an order for a virtual object based on a historical value attribute trend of the virtual object and a scene state of the virtual object scene; for each virtual object in the virtual object set, performing the following processing steps: determining an order placement probability based on the order demand information set corresponding to the virtual object, wherein the order placement probability represents a probability that the virtual object places an order for a virtual object in the virtual object processing scene; generating a virtual object order based on the order placement probability, the object type corresponding to the virtual object, and the order demand information set corresponding to the virtual object, wherein the virtual object order comprises: a virtual order state and virtual order description information; performing order matching on the virtual object order, comprising: determining an order matching state, wherein the order matching state comprises: a real-time matching state and a delayed matching state, the real-time matching state representing on-demand order matching in the order generation order, and the delayed matching state representing delayed order matching; in response to the order matching state being the real-time matching state, performing real-time updating on the order book through the virtual object order to realize order matching, wherein the order book is configured to record virtual object orders that are successfully matched in the virtual object processing scene; in response to the order matching state being the delayed matching state, performing the following delayed order matching steps: performing virtual order matching on the virtual object order to generate a virtual matching record; in response to the order matching state being updated from the delayed matching state to the real-time matching state, updating the order book based on the virtual matching record to realize order matching. In response to the success of the matching, according to the virtual item order, the simulation data recorded by the information recording module corresponding to the virtual item processing scene is updated.

2. The method of claim 1, wherein, After the response to the success of the matching, according to the virtual item order, the virtual item processing scene is updated, the method further comprises: In response to the success of the matching, according to the virtual item order information, the order demand information group corresponding to the virtual object is updated.

3. The method of claim 2, wherein, The method further comprises: In response to the fact that the virtual order state included in each virtual item order in the obtained virtual item order set is an order completion state, and the end time is reached, the simulation data generated between the initial time and the end time is stored, wherein the time interval between the initial time and the end time is a daily granularity time interval.

4. The method of claim 3, wherein, The order demand information in the order demand information group includes: expected value attribute and daily granularity demand amount; and The determination of the order demand information group corresponding to each virtual object in the virtual object set comprises: For each virtual item in the virtual item set, the following benchmark value attribute determination step is performed: In response to the fact that the object type corresponding to the virtual object is a first object type, the benchmark value attribute corresponding to the virtual item is determined based on the first current value attribute corresponding to the virtual item; In response to the fact that the object type corresponding to the virtual object is a second object type, the benchmark value attribute corresponding to the virtual item is determined based on the second current value attribute corresponding to the virtual item; In response to the fact that the object type corresponding to the virtual object is a third object type, the benchmark value attribute corresponding to the virtual item is determined based on the lagging value attribute corresponding to the virtual item; In response to the fact that the object type corresponding to the virtual object is a fourth object type, the benchmark value attribute corresponding to the virtual item is determined based on the first historical value attribute trend corresponding to the virtual item, wherein the first historical value attribute trend is a historical value attribute trend of the virtual item in a historical time period; In response to the fact that the object type corresponding to the virtual object is a fifth object type, the benchmark value attribute corresponding to the virtual item is determined based on the second historical value attribute trend corresponding to the virtual item, wherein the second historical value attribute is a historical value attribute trend of the virtual item at at least two historical time points; The benchmark value attribute corresponding to the virtual item is added with noise to obtain a noise-added benchmark value attribute, which is used as the expected value attribute included in the order demand information in the order demand information group corresponding to the virtual item; According to the noise-added benchmark value attribute and the order placement aggressiveness corresponding to the virtual object, the daily granularity demand amount included in the order demand information corresponding to the noise-added benchmark value in the order demand information group corresponding to the virtual item is generated.

5. The method of claim 4, wherein, The determination of the order placement probability according to the order demand information group corresponding to the virtual object comprises: According to the order demand information group corresponding to the virtual object, a remaining order demand quantity is determined, wherein the remaining order demand quantity represents a total order demand quantity that has not been ordered by the order demand information group; According to the remaining order demand quantity, the order placing probability is determined, wherein the order placing probability and the remaining order demand quantity are positively correlated in a Poisson distribution.

6. An emulation processing device applied to virtual goods, comprising: a scene initialization unit configured to perform scene initialization on a virtual good processing scene in response to reaching an initial time, the virtual good processing scene including a virtual object pool, an order matching module, and an information recording module, the information recording module being used to record simulation data involved in the virtual good processing scene, the simulation data including overall setting data, object information, virtual scene description information, an order book, daily data records, and processing records of virtual goods; a determination unit configured to determine, in response to the scene initialization being completed, an order demand information group corresponding to each virtual object in a virtual object set, wherein the virtual object is to generate a virtual good order for a virtual good through the virtual good processing scene simulation, the order demand information group represents order demand of the virtual object for at least one virtual good, and a type of the virtual object includes a first object type, a second object type, a third object type, a fourth object type, and a fifth object type, wherein the first object type represents that the virtual object randomly places an order for a virtual good, the second object type represents that the virtual object places an order for a virtual good based on a current value attribute of the virtual good, the third object type represents that the virtual object places an order for a virtual good based on a lag value attribute of the virtual good, the fourth object type represents that the virtual object places an order for a virtual good based on a historical value attribute trend of the virtual good, and the fifth object type represents that the virtual object places an order for a virtual good based on a historical value attribute trend of the virtual good and a scene state of the virtual good scene. The execution unit is configured to perform the following processing steps for each virtual object in the virtual object set: determining an order placement probability according to the order demand information set corresponding to the virtual object, wherein the order placement probability represents the probability of placing an order for a virtual item for the virtual object in the virtual item processing scenario; generating a virtual item order according to the order placement probability, the object type corresponding to the virtual object, and the corresponding order demand information set, wherein the virtual item order includes virtual order status and virtual order description information; order matching the virtual item order, including: determining an order matching state, wherein the order matching state includes a real-time matching state and a delayed matching state, the real-time matching state representing on-demand order matching in the order generation order, and the delayed matching state representing delayed order matching; in response to the order matching state being the real-time matching state, updating the order book in real time through the virtual item order to realize order matching, wherein the order book is used to record the virtual item orders that are successfully matched in the virtual item processing scenario; in response to the order matching state being the delayed matching state, performing the following delayed order matching steps: performing virtual order matching on the virtual item order to generate a virtual matching record; in response to the order matching state being updated from the delayed matching state to the real-time matching state, updating the order book according to the virtual matching record to realize order matching; in response to successful matching, updating the simulation data recorded by the information record module corresponding to the virtual item processing scenario according to the virtual item order to realize scene updating. 7.An electronic device, comprising: one or more processors; storage having stored thereon one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-5.

8. A computer readable medium having stored thereon a computer program, wherein, The computer program is executed by the processor to implement the method according to any one of claims 1-5.

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