Internet of vehicles signal simulation method, device and equipment

Abstract

The invention discloses an Internet of Vehicles signal simulation method, device and equipment, and the method comprises the steps: randomly selecting a target travel frequency scene from at least one pre-matched travel frequency scene according to the current date of a vehicle, and carrying out the target travel frequency scene according to the determined vehicle planning frequency; randomly selecting a target driving type scene from the matched driving type scenes each time; random selection and calculation are carried out according to at least one preset driving configuration parameter in the target driving type scene, journey planning is carried out according to the obtained driving planning parameter, and an initial periodic signal is generated within a simulation time range according to an obtained journey planning result, a preset frame rate and a preset signal field configuration rule; judging whether each initial periodic signal meets a triggering condition of any preset triggering scene or not; and if yes, signal field reassignment is carried out based on the initial periodic signal according to a rule in the triggering scene, and a corresponding event signal and an adjusted periodic signal are obtained.

Description

Technical Field

[0001] The present invention relates to a vehicle networking signal simulation method, device and equipment. Background Art

[0002] Internet of Vehicles (IoV) technology combines vehicles with the Internet, mobile communications, sensors and other technologies to achieve information exchange and data sharing between vehicles, between vehicles and road infrastructure, and between vehicles and users. IoV signals ensure that vehicles can achieve multiple functions such as intelligent navigation, safety warning, and vehicle status monitoring in real time. Therefore, IoV signal analysis is of great significance to improving the safety, convenience, comfort and intelligence level of vehicles. With the continuous advancement of technology and the continuous expansion of applications, IoV signal analysis will play a more important role in the future.

[0003] In the pre-research stage of IoV signal analysis, only the relevant definition of the signal can be obtained, but the real IoV signal data cannot be provided. Conventional technology generally adopts the method of developing a program according to data requirements to simulate and obtain simulated IoV signals, or waits for the test vehicle to be developed and then uses the test vehicle to collect IoV signals. Summary of the invention

[0004] In order to obtain an Internet of Vehicles signal that better meets actual needs, an Internet of Vehicles signal simulation method, device and equipment are provided in an embodiment of the present invention.

[0005] In a first aspect, an embodiment of the present invention provides a vehicle networking signal simulation method, which may include:

[0006] According to the current date of the vehicle, randomly select a target travel frequency scenario from at least one pre-matched travel frequency scenario, and determine the vehicle planning frequency according to the target travel frequency scenario;

[0007] According to the number of vehicle planning, a target driving type scenario is randomly selected from the matching driving type scenarios each time;

[0008] Randomly selecting and calculating at least one driving configuration parameter preset in the target driving type scenario to obtain a driving planning parameter;

[0009] Perform itinerary planning according to the driving planning parameters to obtain itinerary planning results;

[0010] According to the trip planning result, within the simulation time range, at a preset frame rate, and according to a preset signal field configuration rule, an initial periodic signal is generated;

[0011] Determining whether each of the initial periodic signals meets the triggering condition of any preset triggering scenario;

[0012] If so, according to the rules in the trigger scenario, reassign the signal field based on the initial periodic signal to obtain the corresponding event signal and the adjusted periodic signal;

[0013] Otherwise, the initial periodic signal is retained.

[0014] In one or some optional embodiments, generating an initial periodic signal according to the trip planning result, within a simulation time range, at a preset frame rate, and according to a preset signal field configuration rule includes:

[0015] According to the trip planning result, generating a basic signal body at a preset frame rate within the simulation time range;

[0016] Determine the value of the key field corresponding to the basic signal body, and based on the value of the key field and the preset signal field configuration rule corresponding to each signal field to be generated, calculate the value of the corresponding signal field, and splice to obtain the initial periodic signal.

[0017] In one or some optional embodiments, determining the value of the key field corresponding to the basic signal body, and calculating the value of the corresponding signal field based on the value of the key field and a preset signal field configuration rule corresponding to each signal field to be generated, and splicing to obtain the initial periodic signal, includes:

[0018] Determine the value of the key field corresponding to the basic signal body, and calculate the value of the corresponding signal field based on the value of the key field and the preset signal field configuration rule corresponding to each signal field to be generated;

[0019] The basic information of the basic signal body, the value of each key field and the value of each signal field are concatenated to obtain an initial periodic signal.

[0020] In one or some optional embodiments, the reassigning of signal fields based on the initial periodic signal according to the rule in the triggering scenario to obtain the corresponding event signal and the adjusted periodic signal includes:

[0021] For each triggering scenario triggered by the initial periodic signal, recalculate the updated value of the corresponding signal field according to the signal field configuration rule of the preset type in the triggering scenario;

[0022] Acquire a tag type of the signal field according to a signal field configuration rule of a preset type in the triggering scenario;

[0023] If the tag type of the signal field is a periodic signal, reassigning the corresponding signal field in the initial periodic signal to a corresponding updated value to obtain an adjusted periodic signal;

[0024] If the tag type of the updated signal field is an event-type signal, the corresponding signal field in the copied initial periodic signal is reassigned to a corresponding updated value to obtain an event-type signal.

[0025] In one or some optional embodiments, if the tag type of the signal field is a periodic signal, reassigning the corresponding signal field in the initial periodic signal to a corresponding update value to obtain an adjusted periodic signal includes:

[0026] If the initial periodic signal triggers multiple trigger scenarios, then for the signal field whose tag type is a periodic signal, determine whether the signal field is updated multiple times in the multiple trigger scenarios. If so, reassign the corresponding signal field in the initial periodic signal to the update value corresponding to the trigger scenario with the latest configuration time to obtain the adjusted periodic signal.

[0027] In one or some optional embodiments, if the tag type of the updated signal field is an event type signal, reassigning the corresponding signal field in the copied initial periodic signal to a corresponding updated value to obtain the event type signal includes:

[0028] If the initial periodic signal triggers multiple trigger scenarios, then for the signal field whose label type is an event-type signal, determine whether the signal field is updated multiple times in the multiple trigger scenarios. If so, for the replacement-type signal field, the corresponding signal field in the copied initial periodic signal is reassigned to the update value corresponding to the trigger scenario with the latest configuration time. For the merged signal field, the corresponding signal field in the copied initial periodic signal is reassigned to the merged update value to obtain the event-type signal; wherein the merged update value is the value obtained by merging the update values ​​of the same merged signal field under the multiple trigger scenarios.

[0029] In one or some optional embodiments, randomly selecting a target travel number scenario from at least one pre-matched travel number scenario according to the current date of the vehicle, and determining the vehicle planning number according to the target travel number scenario includes:

[0030] Determine at least one trip frequency scenario that matches the vehicle, and randomly select a trip frequency scenario corresponding to the current date as the target trip frequency scenario according to the weight according to the date range in the at least one trip frequency scenario; the trip frequency scenario is a scenario configured by the user or set by default that includes a date range and a preset trip frequency range, and each of the trip frequency scenarios is configured with a corresponding weight;

[0031] The planned vehicle number is obtained by randomly selecting from a preset travel number range in the target travel number scenario.

[0032] In one or some optional embodiments, randomly selecting a target driving type scenario from the matching driving type scenarios each time according to the vehicle planning times includes:

[0033] If the number of vehicle planning times is not zero, determining whether the number of actual vehicle planning times reaches the number of vehicle planning times;

[0034] If not, at least one driving type scenario matching the vehicle is determined, and according to the date range in the at least one driving type scenario, a driving type scenario corresponding to the current date is randomly selected according to the weight as the target driving type scenario; the driving type scenario is a user-configured or defaulted scenario containing a date range and at least one preset driving configuration parameter, and each driving type scenario is configured with a corresponding weight;

[0035] If yes, end the vehicle networking signal simulation process within the current date.

[0036] In one or some optional embodiments, the preset driving configuration parameter includes at least one of driving mileage, average speed, driving time, remaining power difference and driving road condition;

[0037] The step of randomly selecting and calculating at least one driving configuration parameter preset in the target driving type scenario to obtain a driving planning parameter includes:

[0038] If the preset driving configuration parameters include driving road conditions, then obtaining planned road condition information of the driving road conditions;

[0039] If the preset driving configuration parameters include an average speed, a random selection is performed according to the parameter range of the average speed to obtain a planned average speed;

[0040] If the preset driving configuration parameters include driving road conditions but do not include average speed, a reference average speed is calculated based on the planned road condition information;

[0041] If the preset driving configuration parameters do not include driving road conditions and average speed, randomly selecting road condition information according to pre-configured basic road condition information to obtain reference road condition information, and calculating a reference average speed according to the reference road condition information;

[0042] If the parameter ranges of the mileage and the driving duration are not empty, randomly selecting from the parameter ranges of the mileage and the driving duration to obtain the planned mileage and the planned driving duration;

[0043] If the parameter ranges of the mileage and the driving duration are empty, and the parameter range of the remaining power difference is not empty, then according to the parameter range of the remaining power difference, a planned remaining power difference is randomly selected, and the planned mileage is calculated according to the planned remaining power difference, and the planned driving duration is calculated in combination with the planned average speed or the reference average speed;

[0044] If the parameter ranges of the mileage, the remaining power difference and the driving duration are all empty, one of the planned mileage and the planned driving duration is randomly selected within the default parameter value range, and the other of the planned mileage and the planned driving duration is calculated in combination with the planned average speed or the reference average speed;

[0045] If the parameter range of the driving mileage is empty, and the parameter ranges of the driving duration and the remaining power difference are not empty, then according to the parameter range of the remaining power difference, a planned remaining power difference is randomly selected, and the planned driving mileage is calculated according to the planned remaining power difference, and the planned driving duration is randomly selected within the parameter range of the driving duration;

[0046] If the parameter range of the difference between the mileage and the remaining power is empty, and the parameter range of the driving duration is not empty, the planned driving duration is randomly selected within the parameter range of the driving duration, and the planned mileage is calculated in combination with the planned average speed or the reference average speed.

[0047] In one or some optional embodiments, the reference road condition information includes the time proportion of each road condition in the driving road condition;

[0048] The road condition information is randomly selected according to the pre-configured basic road condition information to obtain reference road condition information, which specifically includes:

[0049] According to various road conditions in the pre-configured basic road conditions, the time proportion of each road condition is randomly selected within a preset time proportion enumeration value range to obtain the reference road condition information.

[0050] In one or some optional embodiments, performing itinerary planning according to the driving planning parameters to obtain itinerary planning results includes:

[0051] performing driving planning according to the driving planning parameters to obtain a driving planning result;

[0052] Determining the amount of power to be consumed by the vehicle according to the driving planning result;

[0053] If the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than a preset power threshold, determining a charging planning result according to the remaining power of the vehicle;

[0054] A parking planning result is determined according to the driving planning result and / or the charging planning result.

[0055] In one or some optional embodiments, performing driving planning according to the driving planning parameters to obtain a driving planning result includes:

[0056] If the driving planning parameters include the planned average speed, then within the planned driving mileage and planned driving duration, speed distribution is performed according to the planned average speed and a preset acceleration range, and the speed and acceleration at each moment are determined to obtain the driving planning result;

[0057] If the driving planning parameters do not include a planned average speed, determining the road condition time length for each road condition according to the planned road condition information or the reference road condition information;

[0058] Dividing the road condition time length of each road condition into multiple time segments according to the preset time segment length, and obtaining the speed range and acceleration range in each time segment;

[0059] After randomly combining the multiple duration segments and adding a termination duration segment, speed distribution is performed according to the speed range and acceleration range in each duration segment, the speed and acceleration at each moment are determined, and the driving planning result is obtained; the speed of the termination duration segment is reduced from the speed range of the adjacent time period to zero.

[0060] In one or some optional embodiments, in the process of performing speed distribution according to the combined driving duration, the method further includes:

[0061] According to the speed range of two adjacent time segments, determining whether the speeds of the two adjacent time segments jump;

[0062] If so, the speed is adjusted according to the preset acceleration value based on the speed value at the last moment of the previous duration segment from the start moment of the next duration segment until the adjusted speed falls into the speed range corresponding to the next duration segment.

[0063] In one or some optional embodiments, the planned road condition information or the reference road condition information includes a time proportion of each road condition;

[0064] Determining the road condition time length of each road condition according to the planned road condition information or the reference road condition information specifically includes:

[0065] According to the time proportion of each road condition, the time proportion of all road conditions and the planned driving time, the road condition time length of each road condition is obtained.

[0066] In one or some optional embodiments, dividing the road condition time length of each road condition into a plurality of time segments according to a preset time segment length, and obtaining a speed range and an acceleration range in each time segment, includes:

[0067] For each road condition including a continuous duration, the road condition time length is subtracted from the corresponding continuous duration to obtain a remaining time length of the road condition, and the remaining time length of the road condition is divided into a plurality of duration segments according to a preset time segment length to obtain a speed range and an acceleration range within each duration segment; the planned road condition information or the reference road condition information also includes a continuous duration of at least one road condition;

[0068] For each road condition that does not include a continuous duration, the road condition time length is divided into a plurality of duration segments according to a preset time segment length, and a speed range and an acceleration range within each duration segment are obtained.

[0069] In one or some optional embodiments, after randomly combining the multiple duration segments and adding a termination duration segment, speed distribution is performed according to the speed range and acceleration range in each duration segment, and the speed and acceleration at each moment are determined to obtain a driving planning result, including:

[0070] Randomly combine each of the continuous durations and all the duration segments and add a termination duration segment to obtain a combined driving duration;

[0071] Speed ​​distribution is performed according to the speed range and acceleration range in each continuous duration and each duration segment in the combined driving duration, the speed and acceleration at each moment are determined, and the driving planning result is obtained.

[0072] In one or some optional embodiments, if the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than a preset power threshold, determining a charging planning result according to the remaining power of the vehicle includes:

[0073] If the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than a preset power threshold, a charging pile power value is randomly selected from a preset charging pile power enumeration value range;

[0074] A charging power target value is randomly selected from the charging target power range, and the charging time is determined according to the charging pile power value and the remaining power of the vehicle to obtain a charging planning result.

[0075] In one or some optional embodiments, the method further includes determining the simulation time range in the following manner:

[0076] The simulation time range is determined according to the start time of the current vehicle planning, the time range corresponding to the charging planning result and / or the time range corresponding to the driving planning result; wherein:

[0077] The time range corresponding to the charging planning result is determined by the following method:

[0078] Randomly select a waiting time for entering the charging state within the waiting time range for starting the charging state;

[0079] According to the start time of the current vehicle plan, the waiting charging time and the planned charging time, the time range corresponding to the charging plan result is obtained;

[0080] The time range corresponding to the driving planning result is determined by:

[0081] If the trip planning result does not include a charging planning result, a waiting driving time for entering the driving state is randomly selected within the waiting time range for starting the driving state;

[0082] According to the start time of the current vehicle planning, the waiting driving time and the planned driving time, the time range corresponding to the driving planning result is obtained;

[0083] If the trip planning result includes a charging planning result, a waiting driving time for entering the driving state is randomly selected within the waiting time range for starting the driving state;

[0084] According to the start time of the current vehicle planning, the waiting charging time, the planned charging time, the waiting driving time and the planned driving time, the time range corresponding to the driving planning result is obtained.

[0085] In one or some optional embodiments, the method further includes: determining whether the current time is within the time range corresponding to the trip planning result or the time range corresponding to the charging planning result;

[0086] If not, it is determined that the current time is within the time range corresponding to the parking planning result.

[0087] In one or some optional embodiments, the method further includes: outputting frames of the periodic signal obtained within the current date according to a preset output frequency.

[0088] In a second aspect, an embodiment of the present invention provides a device for simulating a vehicle network signal, including:

[0089] A planning frequency module, used to randomly select a target travel frequency scenario from at least one pre-matched travel frequency scenario according to the current date of the vehicle, and determine the vehicle planning frequency according to the target travel frequency scenario;

[0090] A driving scene selection module, used for randomly selecting a target driving type scene from the matching driving type scenes each time according to the number of vehicle planning times;

[0091] A parameter planning module, used to randomly select and calculate at least one driving configuration parameter preset in the target driving type scenario to obtain a driving planning parameter;

[0092] A trip planning module, used to perform trip planning according to the driving planning parameters and obtain a trip planning result;

[0093] A signal generation module, used to generate an initial periodic signal according to the trip planning result, within a simulation time range, at a preset frame rate, and according to a preset signal field configuration rule;

[0094] A judging module, used for judging whether each of the initial periodic signals meets the triggering condition of any preset triggering scenario;

[0095] A signal processing module is used to reassign signal fields based on the initial periodic signal to obtain corresponding event-type signals and adjusted periodic signals if the initial periodic signal meets the triggering conditions of any preset triggering scenario, according to the rules in each triggering scenario triggered by the initial periodic signal; otherwise, retain the initial periodic signal.

[0096] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle network signal simulation method as described in the first aspect.

[0097] In a fourth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the vehicle network signal simulation method as described in the first aspect is implemented.

[0098] In a fifth aspect, an embodiment of the present invention provides a computer program product comprising instructions. When the computer program product is run on a computer device, the computer device executes the vehicle network signal simulation method as described in the first aspect.

[0099] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0100] The embodiment of the present invention provides a method, device and equipment for simulating vehicle network signals. The method randomly selects and calculates the number of trips and driving scenes according to the logical rules of the simulation scene configured by the user, obtains the driving planning parameters to complete the trip planning, generates the signal field according to the preset signal field configuration rules within the time range of the driving planning, obtains the initial periodic signal, and reassigns the value according to the triggering scene to generate the event signal and the adjusted periodic signal. The user can customize the vehicle scene to be simulated, plan different specific driving planning results in the simulation scene in a random manner, and generate the simulation signal according to the preset signal field configuration rules. The user only needs to modify the scene and signal field configuration rules to realize the signal simulation under different logics, and obtain the vehicle network signal covering different vehicle scenes. The collected vehicle network signal has a larger data volume and higher data quality. The user can obtain the vehicle network signal that meets the actual needs by adjusting the scene or modifying the logic of the signal field configuration rules without modifying the code of the simulation program, thereby reducing the technical difficulty of vehicle network signal simulation.

[0101] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0102] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0104] Figure 1 A flowchart of a vehicle networking signal simulation method provided in an embodiment of the present invention;

[0105] Figure 2 It is a schematic diagram of the structure of the vehicle network signal simulation device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0106] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0107] The inventors found that among the existing methods of obtaining vehicle networking signal data, the method of using test vehicles to collect data has a long data collection cycle, a small amount of data collected, and the vehicle scenarios covered by the collected vehicle networking signal data are limited, and the data quality is not guaranteed. The method of collecting data through signal simulation requires the definition of signals and logic in advance based on data requirements, and the development of signal simulation programs in a targeted manner. According to the defined logic, the demand design, coding design, coding, and testing are performed. This method requires the scene and logic of the simulated signal to be fixed, but in the pre-research stage of vehicle networking signal analysis, the scene and logic of signal simulation are uncertain, which leads to a large number of logic modifications and scene additions after the vehicle networking signal data is simulated, greatly increasing the threshold for use and R&D costs. In addition, the internal logic in the signal simulation program is a black box for researchers who do not understand the code, which is not convenient for researchers to modify the logic or adjust the scene. The transformation cost is high, and it is difficult to obtain vehicle networking signals that meet actual needs.

[0108] In view of the above problems, the present invention is proposed to provide a method, device and equipment for simulating vehicle network signals that overcome the above problems or at least partially solve the above problems.

[0109] Embodiment 1

[0110] In the first embodiment of the present invention, a vehicle network signal simulation method is provided, referring to Figure 1 As shown, the method may include the following steps:

[0111] S101: randomly selecting a target travel frequency scenario from at least one pre-matched travel frequency scenario according to the current date of the vehicle, and determining the vehicle planning frequency according to the target travel frequency scenario;

[0112] S102: randomly selecting a target driving type scene from the matching driving type scenes each time according to the number of vehicle planning times;

[0113] S103: randomly selecting and calculating at least one driving configuration parameter preset in the target driving type scenario to obtain a driving planning parameter;

[0114] S104: Perform itinerary planning according to the driving planning parameters to obtain itinerary planning results;

[0115] S105: generating an initial periodic signal according to the trip planning result, within the simulation time range, at a preset frame rate, and according to a preset signal field configuration rule;

[0116] S106: Determine whether each initial periodic signal meets the triggering condition of any preset triggering scenario; if so, execute step S107; otherwise, execute step S108;

[0117] S107: reassigning signal fields based on the initial periodic signal according to the rules in the triggering scenario to obtain a corresponding event signal and an adjusted periodic signal;

[0118] S108: retain the initial periodic signal.

[0119] In an optional embodiment, in the above step S101, according to the current date of the vehicle, a target travel frequency scenario is randomly selected from at least one pre-matched travel frequency scenario, and the vehicle planning frequency is determined according to the target travel frequency scenario, including:

[0120] Determine at least one trip frequency scenario that matches the vehicle, and randomly select a trip frequency scenario corresponding to the current date as the target trip frequency scenario according to the weight based on the date range in the at least one trip frequency scenario; the trip frequency scenario is a user-configured or defaulted scenario that includes a date range and a preset trip frequency range, and each trip frequency scenario is configured with a corresponding weight;

[0121] The vehicle planning number is randomly selected from the preset travel number range in the target travel number scenario.

[0122] In an embodiment of the present invention, the user can configure a configuration trip number scenario including a date range and a trip number range in the database before executing the vehicle network signal simulation, or can perform a system default configuration when developing a signal simulation program, configure a default trip number scenario in the database, and the date range and the preset trip number range are set by default in the default number scenario. For example, in the configuration trip scenario 1 configured by the user, the date range is Monday to Thursday, and the preset trip number is 1 to 2 times; in the configuration trip scenario 2 configured by the user, the date range is Monday to Sunday, and the preset trip number is 0 to 6 times; in the default trip number scenario, the date range is Monday to Sunday, and the preset trip number is 0 to 5 times.

[0123] In an embodiment of the present invention, one or more travel frequency scenarios may be configured for each vehicle or each type of vehicle. When configuring each configured travel frequency scenario, a weight value may be assigned to the configured travel frequency scenario. For example, the weight value assigned to the configured travel frequency scenario 1 is 100, and the weight value assigned to the configured travel frequency scenario 2 is 10. Then, the probability of each configured travel frequency scenario being randomly selected is: the weight of the configured travel frequency scenario / (100*the number of configured travel frequency scenarios that match the vehicle on the current date), and the probability of the default travel frequency scenario being randomly selected is = 1-the sum of the probabilities of all configured travel frequency scenarios that match the vehicle on the current date, that is, when randomly selecting the target travel frequency scenario, the travel frequency scenario with a larger weight is more likely to be selected.

[0124] Based on this, at least one trip frequency scenario matching the vehicle is determined, and according to the date range in at least one trip frequency scenario, a trip frequency scenario corresponding to the current date is randomly selected by weight as the target trip frequency scenario. Specifically, for the vehicle to be simulated, all trip frequency scenarios matching the vehicle are selected from the database according to the vehicle identification information, and then according to the current date (the current date is the day of the week), a trip frequency scenario with a date range including the current date is randomly selected by weight from all the trip frequency scenarios matching the vehicle as the target trip frequency scenario. For example, it can be that when the simulation is first started or at a fixed time node every day (such as 0 o'clock every day), a target trip frequency scenario is randomly found according to the probability calculated based on the above probability calculation method among all the trip frequency scenarios matching the vehicle on the current date.

[0125] After obtaining the target travel frequency scenario, a vehicle planning number is randomly selected from the preset travel number range in the target travel frequency scenario. When the randomly determined vehicle planning number is equal to 0, no vehicle travel is planned on the current date; when the randomly determined vehicle planning number > 0, the subsequent vehicle planning process is executed.

[0126] In an embodiment of the present invention, the start time and end time of each vehicle trip within the date range can also be configured in the above-mentioned trip frequency scenario, so that when the vehicle driving plan is subsequently executed, the travel time of the first planned trip on the current date is constrained to be no earlier than the start time, and the travel time of the last planned trip on the current date can also be constrained to be no later than the end time.

[0127] In an optional embodiment, in the above step S102, according to the number of vehicle planning, a target driving type scene is randomly selected from the matching driving type scenes each time, including:

[0128] If the number of vehicle planning times is not zero, then determine whether the number of actual vehicle planning times reaches the number of vehicle planning times;

[0129] If not, at least one driving type scenario matching the vehicle is determined, and according to the date range in the at least one driving type scenario, a driving type scenario corresponding to the current date is randomly selected according to the weight as the target driving type scenario; the driving type scenario is a user-configured or defaulted scenario containing a date range and at least one preset driving configuration parameter, and each driving type scenario is configured with a corresponding weight;

[0130] If yes, end the vehicle networking signal simulation process within the current date.

[0131] In the embodiment of the present invention, the driving type scene can be a scene named by the user, for example, it can be named as a commuting scene, a travel scene, and other scenes. Before executing the vehicle network signal simulation, the user can configure one or more driving type scenes for each vehicle or each type of vehicle in the database, or the user can configure the default driving type scene in the database by making a system default configuration when developing the signal simulation program.

[0132] In an embodiment of the present invention, a weight value may be assigned to the driving type scene when configuring each driving type scene. For example, the weight value assigned to driving type scene one is 100, and the weight value assigned to driving type scene two is 10. Then, the probability of each driving type scene being randomly selected is: the weight of the driving type scene / (100*the number of driving type scenes that match the vehicle on the current date), and the probability of the default driving type scene being randomly selected is = 1-the sum of the probabilities of all user-configured driving type scenes that match the vehicle on the current date, that is, when randomly selecting the target driving type scene, the driving type scene with a larger weight is more likely to be selected.

[0133] Based on this, if it is determined that the number of vehicle planning times is not zero, the first vehicle planning is started, at least one driving type scenario matching the vehicle is determined, and according to the date range in at least one driving type scenario, a driving type scenario corresponding to the current date is randomly selected by weight as the target driving type scenario. Specifically, for the vehicle to be simulated, all driving type scenarios matching the vehicle are selected from the database according to the vehicle identification information, and then according to the current date (the current date is the day of the week), a driving type scenario with a date range including the current date is randomly selected by weight from all driving type scenarios matching the vehicle as the target driving type scenario. For example, it can be that when the simulation is first started or at a fixed time node every day (such as 0 o'clock every day), a target driving type scenario is randomly found according to the probability calculated based on the above probability calculation method among all driving type scenarios matching the vehicle on the current date. Then, based on the target driving scenario, the itinerary planning and vehicle network signal simulation process of steps S103 to S108 are performed.

[0134] After each execution of the Internet of Vehicles signal simulation process, it is determined whether the number of simulated vehicle trips reaches the above-mentioned randomly generated number of vehicle planning. If not, the process of determining the target driving type scenario in the above-mentioned step S102 is re-executed, and the itinerary planning and Internet of Vehicles signal simulation process of steps S103 to S108 are re-executed based on the newly determined target driving scenario.

[0135] In the embodiment of the present invention, the driving type scenario can be a scenario named by the user, for example, it can be named as a commuting scenario, a travel scenario, and other scenarios. Before executing the vehicle network signal simulation, the user can configure the date range in the driving type scenario and the range of the preset type of driving configuration parameters when configuring the driving type scenario for each vehicle or each type of vehicle in the database. When performing the system default configuration when developing the signal simulation program, the date range and the range of the preset type of driving configuration parameters can also be set in the default driving type scenario. Among them, the type of driving configuration parameters can include at least one of mileage, average speed, driving time, remaining power difference, and driving road conditions.

[0136] In the embodiment of the present invention, the above-mentioned remaining power difference (SOC difference) is the percentage of the battery rated power required for a trip. For example, if the remaining power difference set by the user is 20, it means that the power required for the trip is 20% of the battery rated power. In the embodiment of the present invention, the cruising range of 100% of the battery rated power is called the vehicle standard cruising range. According to the remaining power difference and the vehicle standard cruising range, the mileage corresponding to the power consumed by the vehicle corresponding to the remaining power difference can be converted, that is, the mileage S = remaining power difference / 100*vehicle standard cruising range.

[0137] Referring to Table 1, assuming that there are 5 types of road conditions configured in the basic road condition, the basic road condition configuration includes the vehicle speed range and acceleration range under each road condition. In the user-configured driving type scenario or the default driving type scenario, the time proportion of each road condition can be configured, or the time proportion and continuous duration of each road condition can be configured.

[0138] Table 1 Basic road condition configuration

[0139]

[0140] For example, in the user-configured driving type scenario one, the date range is Monday to Thursday, the mileage range is 3 kilometers to 10 kilometers, the driving time range is 10 minutes to 20 minutes, and there is no road condition configuration; in the user-configured driving type scenario two, the date range is Monday to Sunday, the mileage range is 10 kilometers to 65 kilometers, the driving conditions are rural and urban, and the time proportion of rural and urban is 50% each; in the default driving type scenario, the date range is Monday to Sunday, the mileage range is 5 kilometers to 40 kilometers, and there is no road condition configuration.

[0141] In an optional embodiment, in the above step S103, randomly selecting and calculating at least one driving configuration parameter preset in the target driving type scenario to obtain driving planning parameters includes:

[0142] If the preset driving configuration parameters include driving road conditions, then obtaining planned road condition information of the driving road conditions;

[0143] If the preset driving configuration parameters include average speed, a random selection is performed according to the parameter range of the average speed to obtain the planned average speed;

[0144] If the preset driving configuration parameters include driving road conditions but do not include average speed, the reference average speed is calculated based on the planned road condition information;

[0145] If the preset driving configuration parameters do not include driving road conditions and average speed, the road condition information is randomly selected according to the pre-configured basic road condition information to obtain reference road condition information, and the reference average speed is calculated according to the reference road condition information;

[0146] If the parameter ranges of the mileage and the driving duration are not empty, a random selection is made from the parameter ranges of the mileage and the driving duration to obtain the planned mileage and the planned driving duration;

[0147] If the parameter ranges of the mileage and driving duration are empty, and the parameter range of the remaining power difference is not empty, then the planned remaining power difference is randomly selected according to the parameter range of the remaining power difference, and the planned mileage is calculated based on the planned remaining power difference, and the planned driving duration is calculated in combination with the planned average speed or the reference average speed;

[0148] If the parameter ranges of mileage, remaining power difference and driving time are all empty, one of the planned mileage and planned driving time is randomly selected within the default parameter value range, and the other of the planned mileage and planned driving time is calculated in combination with the planned average speed or the reference average speed;

[0149] If the parameter range of the driving mileage is empty, and the parameter ranges of the driving time and the remaining power difference are not empty, then according to the parameter range of the remaining power difference, the planned remaining power difference is randomly selected, and the planned driving mileage is calculated according to the planned remaining power difference, and the planned driving time is randomly selected within the parameter range of the driving time;

[0150] If the parameter range of the difference between the mileage and the remaining power is empty, and the parameter range of the driving time is not empty, the planned driving time is randomly selected within the parameter range of the driving time, and the planned mileage is calculated in combination with the planned average speed or the reference average speed.

[0151] In the embodiment of the present invention, different strategies can be adopted when performing travel planning according to different parameter information configured in the randomly generated target travel type scene. Among them:

[0152] The first case: if the preset driving configuration parameters in the target driving type scenario include average speed and driving road conditions, the planned road condition information of the driving road conditions is obtained, and a random selection is performed according to the parameter range of the average speed to obtain the planned average speed; at this time, further:

[0153] If the parameter ranges of the mileage and the driving duration are not empty, a random selection is made from the parameter ranges of the mileage and the driving duration to obtain the planned mileage and the planned driving duration;

[0154] If the parameter ranges of the mileage and driving duration are empty, and the parameter range of the remaining power difference is not empty, then the planned remaining power difference is randomly selected according to the parameter range of the remaining power difference, and the planned mileage is calculated based on the planned remaining power difference, and the planned driving duration is calculated in combination with the planned average speed;

[0155] If the parameter ranges of mileage, remaining power difference and driving time are all empty, then one of the planned mileage and planned driving time is randomly selected within the default parameter value range, and the other of the planned mileage and planned driving time is calculated in combination with the planned average speed;

[0156] If the parameter range of the driving mileage is empty, and the parameter ranges of the driving time and the remaining power difference are not empty, then according to the parameter range of the remaining power difference, the planned remaining power difference is randomly selected, and the planned driving mileage is calculated according to the planned remaining power difference, and the planned driving time is randomly selected within the parameter range of the driving time;

[0157] If the parameter range of the difference between the mileage and the remaining power is empty, and the parameter range of the driving time is not empty, the planned driving time is randomly selected within the parameter range of the driving time, and the planned mileage is calculated in combination with the planned average speed.

[0158] The second case: If the preset driving configuration parameters in the target driving type scenario include average speed but not driving road conditions, then only the parameter range of the average speed is randomly selected to obtain the planned average speed. In addition, it is not necessary to randomly generate reference road condition information based on the basic road conditions. In this case, further:

[0159] If the parameter ranges of the mileage and the driving duration are not empty, a random selection is made from the parameter ranges of the mileage and the driving duration to obtain the planned mileage and the planned driving duration;

[0160] If the parameter ranges of the mileage and driving duration are empty, and the parameter range of the remaining power difference is not empty, then the planned remaining power difference is randomly selected according to the parameter range of the remaining power difference, and the planned mileage is calculated based on the planned remaining power difference, and the planned driving duration is calculated in combination with the planned average speed;

[0161] If the parameter ranges of mileage and driving duration are both empty, one of the planned mileage and driving duration is randomly selected within the default parameter value range, and the other of the planned mileage and driving duration is calculated in combination with the planned average speed;

[0162] If one of the parameter ranges of mileage, remaining power difference and driving time is not empty, then one of the planned mileage and the planned driving time is randomly selected within the value range of the corresponding parameter, and the other of the planned mileage and the planned driving time is calculated in combination with the planned average speed;

[0163] If the parameter range of the driving mileage is empty, and the parameter ranges of the driving time and the remaining power difference are not empty, then according to the parameter range of the remaining power difference, the planned remaining power difference is randomly selected, and the planned driving mileage is calculated according to the planned remaining power difference, and the planned driving time is randomly selected within the parameter range of the driving time;

[0164] If the parameter range of the difference between the mileage and the remaining power is empty, and the parameter range of the driving time is not empty, the planned driving time is randomly selected within the parameter range of the driving time, and the planned mileage is calculated in combination with the planned average speed.

[0165] The third case: if the preset driving configuration parameters include driving road conditions but do not include average speed, the reference average speed is calculated based on the planned road condition information; at this time, further:

[0166] If the parameter ranges of the mileage and the driving duration are not empty, a random selection is made from the parameter ranges of the mileage and the driving duration to obtain the planned mileage and the planned driving duration;

[0167] If the parameter ranges of the mileage and driving duration are empty, and the parameter range of the remaining power difference is not empty, then the planned remaining power difference is randomly selected according to the parameter range of the remaining power difference, and the planned mileage is calculated based on the planned remaining power difference, and the planned driving duration is calculated in combination with the planned average speed or the reference average speed;

[0168] If the parameter ranges of mileage, remaining power difference and driving time are all empty, one of the planned mileage and planned driving time is randomly selected within the default parameter value range, and the other of the planned mileage and planned driving time is calculated in combination with the reference average speed;

[0169] If the parameter range of the driving mileage is empty, and the parameter ranges of the driving time and the remaining power difference are not empty, then according to the parameter range of the remaining power difference, the planned remaining power difference is randomly selected, and the planned driving mileage is calculated according to the planned remaining power difference, and the planned driving time is randomly selected within the parameter range of the driving time;

[0170] If the parameter range of the difference between the mileage and the remaining power is empty, and the parameter range of the driving time is not empty, the planned driving time is randomly selected within the parameter range of the driving time, and the planned mileage is calculated in combination with the reference average speed.

[0171] Fourth case: if the preset driving configuration parameters do not include driving road conditions and average speed, then the road condition information is randomly selected according to the pre-configured basic road condition information to obtain reference road condition information, and the reference average speed is calculated according to the reference road condition information; at this time, further:

[0172] If the parameter ranges of the mileage and the driving duration are not empty, a random selection is made from the parameter ranges of the mileage and the driving duration to obtain the planned mileage and the planned driving duration;

[0173] If the parameter ranges of the mileage and driving duration are empty, and the parameter range of the remaining power difference is not empty, then the planned remaining power difference is randomly selected according to the parameter range of the remaining power difference, and the planned mileage is calculated based on the planned remaining power difference, and the planned driving duration is calculated in combination with the planned average speed or the reference average speed;

[0174] If the parameter ranges of mileage, remaining power difference and driving time are all empty, one of the planned mileage and planned driving time is randomly selected within the default parameter value range, and the other of the planned mileage and planned driving time is calculated in combination with the reference average speed;

[0175] If the parameter range of the driving mileage is empty, and the parameter ranges of the driving time and the remaining power difference are not empty, then according to the parameter range of the remaining power difference, the planned remaining power difference is randomly selected, and the planned driving mileage is calculated according to the planned remaining power difference, and the planned driving time is randomly selected within the parameter range of the driving time;

[0176] If the parameter range of the difference between the mileage and the remaining power is empty, and the parameter range of the driving time is not empty, the planned driving time is randomly selected within the parameter range of the driving time, and the planned mileage is calculated in combination with the planned average speed or the reference average speed.

[0177] In the embodiment of the present invention, in the third case or the fourth case, if the parameter ranges of the mileage and the driving duration are not empty, the reference average speed may be omitted and the planned average speed may be calculated based on the randomly calculated planned mileage and planned driving duration.

[0178] In a specific embodiment, the reference road condition information includes the time proportion of each road condition in the driving road condition;

[0179] The above-mentioned randomly selecting the traffic condition information according to the pre-configured basic traffic condition information to obtain the reference traffic condition information specifically includes:

[0180] According to various road conditions in the pre-configured basic road conditions, the time proportion of each road condition is randomly selected within a preset time proportion enumeration value range to obtain reference road condition information.

[0181] For example, the time proportion enumeration values ​​within the above preset time proportion enumeration value range may be 0, 1, and 2. Referring to Table 1, according to the five road conditions (highway, fast, urban, rural, and mountainous) in the basic road conditions, the time proportion of each road condition is randomly set within 0, 1, and 2, and the speed range and acceleration range of each road condition adopt the default configuration in Table 1. For example, if the time proportion values ​​of the three randomly selected road conditions are 0, 1, and 2, respectively, the time proportion weight of the first road condition is 0, the time proportion weight of the second road condition is 1 / (1+2)=33%, and the time proportion weight of the third road condition is 66%.

[0182] Furthermore, in extreme cases, if the time proportions of multiple randomly selected road conditions are all 0, then one of the road conditions is randomly selected, and the time proportion of the randomly selected road condition is set to 1, and the time proportions of the remaining road conditions are set to 0, that is, the time proportion weight of the randomly selected road condition is 100%.

[0183] In a specific embodiment, if the parameter ranges of mileage and driving duration are both empty, the default parameter value range of mileage can be, for example, 5 kilometers to 40 kilometers, and the default parameter value range of driving duration can be, for example, 1 minute to 60 minutes. When performing random selection, the priority of mileage can be set higher than driving duration, so that the planned driving duration can be calculated based on the randomly selected planned mileage.

[0184] In a specific embodiment, the reference average speed is calculated according to the planned road condition information or the reference road condition information. Specifically, the reference average speed V is calculated based on the following formula according to the speed range and time proportion of each road condition in the planned road condition information or the reference road condition information. c :

[0185]

[0186] Wherein, N is the number of road conditions in the planned road condition information or the reference road condition information, n is the nth road condition in the planned road condition information or the reference road condition information, n is a positive integer less than or equal to N, V n max represents the maximum speed of the nth road condition, V n min represents the minimum speed of the nth road condition, P n Indicates the time proportion of the nth road condition.

[0187] For example, assuming that the planned traffic information or reference traffic information includes both rural and urban traffic conditions, and the time proportion of each of the two traffic conditions is 50%, then the calculated average speed is:

[0188] V c =(50*(40+0) / 2+50*(60+30) / 2) / (50+50)=32.5km / h.

[0189] In an optional embodiment, in the above step S104, itinerary planning is performed according to the driving planning parameters to obtain itinerary planning results, which may specifically include:

[0190] Perform driving planning according to driving planning parameters to obtain driving planning results;

[0191] Determine the amount of electricity to be consumed by the vehicle based on the driving planning results;

[0192] If the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than the preset power threshold, the charging planning result is determined according to the remaining power of the vehicle;

[0193] The parking planning result is determined according to the driving planning result and / or the charging planning result.

[0194] In a specific embodiment, the driving planning is performed according to the driving planning parameters to obtain the driving planning result, which specifically includes:

[0195] If the driving planning parameters include the planned average speed, the speed distribution is performed according to the planned average speed and the preset acceleration range within the planned driving mileage and planned driving duration, and the speed and acceleration at each moment are determined to obtain the driving planning result.

[0196] In an embodiment of the present invention, when speed distribution is performed according to the planned average speed and the preset acceleration range to determine the speed and acceleration at each moment, the speed at the current moment can be calculated based on the speed at the previous moment and the acceleration randomly obtained from the preset acceleration range, wherein the preset acceleration range fluctuates between ±(0.5%-1%) of the planned average speed. It should be noted that when the acceleration is randomly obtained according to the preset acceleration range, it is necessary to constrain the obtained acceleration to satisfy the requirement that the speed at the current moment will not exceed the floating range of the planned average speed, and the floating range of the planned average speed can be, for example, a floating range of ±10% of the planned average speed.

[0197] In an optional embodiment, in the above step S104, performing itinerary planning according to at least one driving planning parameter to obtain a driving planning result may further include:

[0198] If the planned average speed is not included in the driving planning parameters, the road condition time length for each road condition is determined based on the planned road condition information or the reference road condition information;

[0199] Dividing the road condition time length of each road condition into multiple time segments according to the preset time segment length, and obtaining the speed range and acceleration range in each time segment;

[0200] After randomly combining multiple duration segments and adding a termination duration segment, the speed distribution is performed according to the speed range and acceleration range in each duration segment, the speed and acceleration at each moment are determined, and the driving planning result is obtained; the speed of the termination duration segment is reduced from the speed range of the adjacent time period to zero.

[0201] In a specific embodiment, in the process of performing speed distribution according to the combined driving duration, the method further includes:

[0202] According to the speed range of two adjacent time segments, it is determined whether the speed of two adjacent time segments jumps;

[0203] If so, the speed is adjusted according to the preset acceleration value based on the speed value at the last moment of the previous duration segment from the start moment of the next duration segment until the adjusted speed falls into the speed range corresponding to the next duration segment.

[0204] Specifically, when switching from the current duration segment to the next duration segment, the speed range of the current duration segment and the speed range of the next segment will be compared to determine whether the speed jump occurs. For example, the speed range of the current duration segment is 40km / h-60km / h, and the speed range of the next duration segment is 100km / h-150km / h, then there is a speed jump.

[0205] If the minimum speed range of the current duration segment is greater than the maximum speed range of the next duration segment, the speed of the next duration segment is increased from the beginning of the next duration segment based on the speed value at the end of the previous duration segment according to the preset acceleration (e.g. -1.435 m / s 2 ) to decelerate until the speed drops to the speed range of the next time segment, and the acceleration returns to the acceleration randomly obtained within the acceleration range of the time segment;

[0206] Similarly, if the maximum value of the speed range of the current time segment is less than the minimum value of the speed range of the next time segment, the speed value at the end of the previous time segment is increased according to the preset acceleration (for example, 1.435 m / s) from the beginning of the next time segment. 2 ) to accelerate until the speed increases to the speed range of the next time segment, and the acceleration returns to the acceleration randomly obtained within the acceleration range within the time segment.

[0207] In a specific embodiment, the planned road condition information or the reference road condition information includes the time proportion of each road condition;

[0208] The above-mentioned determining the length of the road condition time for each road condition based on the planned road condition information or the reference road condition information specifically includes:

[0209] According to the time proportion of each road condition, the time proportion of all road conditions and the planned driving time, the road condition time length of each road condition is obtained.

[0210] In an embodiment of the present invention, the road condition time length of each road condition in this driving plan can be calculated based on the planned driving time and the time proportion of each road condition. The time proportion of each road condition refers to the time proportion of each road condition included in the planned road condition information or the reference road condition information. The specific calculation method can be road condition time length = planned travel time * time proportion of road condition / sum of time proportions of all road conditions in the planned driving time. After the road condition time length is determined, the road condition time length of each road condition can be divided into multiple time segments according to the preset time segment length to obtain the speed range and acceleration range in each time segment. The speed range and acceleration range in each time segment are the speed range and acceleration range of the road condition.

[0211] In a specific embodiment, the road condition time length of each road condition is divided into multiple time segments according to the preset time segment length, and the speed range and acceleration range in each time segment are obtained, which may specifically include:

[0212] For each road condition including a continuous duration, the corresponding continuous duration is subtracted from the road condition time length to obtain the remaining time length of the road condition, and the remaining time length of the road condition is divided into a plurality of time segments according to a preset time segment length to obtain a speed range and an acceleration range within each time segment; the planned road condition information or the reference road condition information also includes the continuous duration of at least one road condition;

[0213] For each road condition that does not include a continuous duration, the road condition time length is divided into a plurality of duration segments according to a preset time segment length, and a speed range and an acceleration range within each duration segment are obtained.

[0214] After randomly combining multiple duration segments and adding a terminal duration segment, the speed distribution is performed according to the speed range and acceleration range in each duration segment, the speed and acceleration at each moment are determined, and the driving planning result is obtained, which may specifically include:

[0215] Each continuous duration and all duration segments are randomly combined and a terminal duration segment is added to obtain the combined driving duration;

[0216] The speed distribution is performed according to the speed range and acceleration range in each continuous duration and each duration segment in the combined driving duration, the speed and acceleration at each moment are determined, and the driving planning result is obtained.

[0217] For example, assuming that a certain road condition is configured with a continuous duration a, before the road condition time length of the road condition is divided into multiple duration segments according to the preset time segment length, the continuous duration a is first deducted from the road condition time length of the road condition to obtain the remaining part of the road condition time length A. Assuming that the planned driving time also includes other road condition time lengths B, C and D that are not configured with continuous durations, the four road condition time lengths A, B, C and D are put into a set, and all the road condition durations in the set are divided into multiple duration segments (A1, A2, A3...B1, B2, B3...) according to the preset time segment length (for example, 30 seconds). These duration segments and the continuous duration a are randomly shuffled and combined together, and finally a termination duration segment is added. The time length of the termination duration segment can be selected according to the actual situation, for example, it can be equal to the above-mentioned preset time segment length.

[0218] In the embodiment of the present invention, the speed of the end time segment is a time segment in which the speed range of the adjacent time segment is reduced to zero, so that the parking effect can be simulated. Thus, the signal simulation can be performed to obtain the Internet of Vehicles signal in the parking scene.

[0219] In a specific embodiment, if the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than the preset power threshold, the charging planning result is determined according to the remaining power of the vehicle, including:

[0220] If the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than the preset power threshold, a charging pile power value is randomly selected from the preset charging pile power enumeration value range;

[0221] A charging power target value is randomly selected from the charging target power range, and the charging time is determined according to the charging pile power value and the remaining power of the vehicle to obtain the charging planning result.

[0222] In the embodiment of the present invention, after completing a driving plan and obtaining the driving plan result, the rated battery power consumed to execute the trip, i.e., the vehicle power to be consumed, can be calculated based on the driving plan result. The vehicle power to be consumed soc = planned driving mileage / standard cruising range. It is known that the vehicle power soc corresponding to the standard cruising range is 100% of the battery rated power, and the vehicle power to be consumed can be obtained.

[0223] In addition, the remaining power of the vehicle at the end of the last charge and discharge before the current driving plan begins can be obtained from the database. If the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than the preset power threshold (for example, 10% of the rated power of the battery), then a charging scenario will be planned before simulating the trip. Specifically, it can be that a charging pile power value is randomly selected from the preset charging pile power enumeration value range, wherein the charging pile power enumeration value can be obtained from the basic information of the charging pile preset in the database, for example, the preset charging pile power enumeration value range obtained is 60kW, 150kW, 220kW and 350kW, then the charging pile power value of 60kW, 150kW, 220kW or 350kW can be randomly obtained, and a charging power target value is randomly selected from the charging target power range, assuming that the charging target power range is max ((vehicle power to be consumed + 10% battery rated power), 80% battery rated power) to 100% battery rated power, then the charging power target value can be randomly obtained, and the charging time is determined according to the charging pile power value, the remaining power of the vehicle and the charging power target value, and the charging planning result is obtained. The charging planning result is the charging time corresponding to charging according to the randomly generated charging pile power value.

[0224] In the embodiment of the present invention, the above charging time = (charging power target value - vehicle remaining power) * battery rated power / charging pile power value. During the charging process, the vehicle remaining power soc will increase linearly with the increase of charging time (the power of each frame of data can fluctuate by 10%).

[0225] In a specific embodiment, the simulation time range is determined by:

[0226] The simulation time range is determined according to the start time of the current vehicle planning, the time range corresponding to the charging planning result and / or the time range corresponding to the driving planning result; wherein:

[0227] Determine the time range corresponding to the charging planning result in the following way:

[0228] Randomly select a waiting time for entering the charging state within the waiting time range for starting the charging state;

[0229] According to the waiting charging time and the planned charging time, the time range corresponding to the charging planning result is obtained.

[0230] The time range corresponding to the driving planning result is determined by:

[0231] If the trip planning result does not include a charging planning result, a waiting driving time for entering the driving state is randomly selected within the waiting time range for starting the driving state;

[0232] According to the start time of the current vehicle planning, the waiting driving time and the planned driving time, the time range corresponding to the driving planning result is obtained;

[0233] If the trip planning result includes a charging planning result, a waiting driving time for entering the driving state is randomly selected within the waiting time range for starting the driving state;

[0234] According to the start time of the current vehicle planning, the waiting charging time, the planned charging time, the waiting driving time and the planned driving time, the time range corresponding to the driving planning result is obtained.

[0235] In an embodiment of the present invention, if the trip planning result includes the charging planning result, the simulation time range is determined according to the time range corresponding to the charging planning result and the time range corresponding to the driving planning result. If the trip planning result does not include the charging planning result, the simulation time range is determined according to the time range corresponding to the driving planning result.

[0236] In an embodiment of the present invention, after obtaining the driving planning result according to the above steps, it is further determined that the vehicle needs to be charged, and after obtaining the charging planning result, after the charging planning is completed, a waiting time for entering the charging state can be randomly selected from the waiting time range for starting charging, and the starting time of the time range corresponding to the above charging planning result is the time from the starting time of the current vehicle planning to the waiting time of the charging state, and the ending time of the time range corresponding to the above charging planning result is the time from the starting time of the current vehicle planning to the waiting time of the charging state and the planned charging time, thereby obtaining the time range corresponding to the charging planning result. Assuming that the waiting time range for starting the charging state is 1 minute to 5 minutes, and the waiting time for entering the charging state randomly selected from the range is 2 minutes, the starting time of the time range corresponding to the charging planning result will be reached after 2 minutes after the starting time of the current vehicle planning, and the simulated charging state will be entered, and the vehicle charging process corresponding to the simulated charging planning result will be executed. When the simulated charging time reaches the planned charging time, the ending time of the time range corresponding to the charging planning result will be reached.

[0237] Because the charging planning is performed before the current vehicle travels, after determining the time range corresponding to the charging planning result, the time range corresponding to the driving planning result can be further determined. Specifically, at the moment when the charging planning is completed, a waiting time for entering the driving state can be randomly selected from the waiting time range for starting the driving state. The starting time of the time range corresponding to the above driving planning result is the time from the start time of the current vehicle planning through the waiting time for the charging state, the planned charging time, and the waiting time for entering the driving state. The end time of the time range corresponding to the above driving planning result is the time from the start time of the current vehicle planning through the waiting time for the charging state, the planned charging time, the waiting time for entering the driving state, and the planned driving time, thereby obtaining the time range corresponding to the driving planning result. Assuming that the waiting time for starting the driving state ranges from 1 minute to 30 minutes, and the waiting time for entering the driving state randomly selected from the range is 20 minutes, then after the start time of the current vehicle planning, the waiting time of the charging state, the planned charging time plus 20 minutes will pass, and the start time of the time range corresponding to the driving planning result will be reached. The simulated driving state will be started, and the vehicle driving process corresponding to the driving planning result will be simulated. When the simulated driving time reaches the planned driving time, the end time of the time range corresponding to the driving planning result will be reached.

[0238] For example, assuming that the start time of the current vehicle planning is 8:00, and a waiting time of 2 minutes is randomly selected from the waiting time range of the start charging state, then the simulated charging state will begin at 8:02. Assuming that the charging time in the current charging planning result is 40 minutes, the end time of this charging is 8:42, so the time range corresponding to this charging planning result is determined to be 8:02 to 8:42. The end time of this charging is 8:42, and the waiting time of 20 minutes is randomly selected from the waiting time range of the start driving state, then the simulated driving state will begin at 9:02. Assuming that the planned driving time of this driving plan is 40 minutes, the end time of this trip is 9:42, so the time range corresponding to this driving planning result is determined to be 9:02 to 9:42.

[0239] In an embodiment of the present invention, if after obtaining the driving planning results according to the above steps, it is determined that the vehicle does not need to be charged, that is, there is no charging plan in the current vehicle planning, and the above trip planning results do not include the charging planning results, then after completing a driving planning and obtaining the driving planning results, a waiting time for entering the driving state can be randomly selected from the waiting time range for starting the driving state after the planning is completed. The starting time of the time range corresponding to the above driving planning result is the time from the start time of the current vehicle planning through the waiting time for entering the driving state, and the end time of the time range corresponding to the above driving planning result is the time from the start time of the current vehicle planning through the waiting time for entering the driving state and the planned driving time, thereby obtaining the time range corresponding to the driving planning result.

[0240] Assuming that the waiting time for starting the driving state ranges from 1 minute to 30 minutes, and the waiting time for randomly entering the driving state from this range is 20 minutes, then 20 minutes after the driving planning result is obtained, the start time of the time range corresponding to the driving planning result will be reached, and the simulated driving state will be started. The vehicle driving process corresponding to the driving planning result will be simulated. When the simulated driving time reaches the planned driving time, the end time of the time range corresponding to the driving planning result will be reached.

[0241] For example, assuming that the start time of this driving plan is 8 o'clock, after the planning is completed, a waiting time of 20 minutes is randomly selected within the waiting time range for starting the driving state, and the simulated driving state is started at 8:20. Assuming that the planned driving time of this driving plan is 40 minutes, the end time of this trip is 9 o'clock, thereby determining that the time range corresponding to the result of this driving plan is from 8:20 to 9 o'clock.

[0242] In a specific embodiment, the method further includes: determining whether the current time is within a time range corresponding to the trip planning result or a time range corresponding to the charging planning result;

[0243] If not, it is determined that the current time is within the time range corresponding to the parking planning result.

[0244] In the embodiment of the present invention, on the current date, if it is determined that the current time is neither within the time range corresponding to the travel planning result nor within the time range corresponding to the charging planning result, it indicates that the vehicle is in a parked state.

[0245] In an optional embodiment, in the above step S105, according to the itinerary planning result, within the simulation time range, at a preset frame rate, and according to a preset signal field configuration rule, an initial periodic signal is generated, specifically including:

[0246] According to the trip planning results, the basic signal body is generated at the preset frame rate within the simulation time range;

[0247] Determine the value of the key field corresponding to the basic signal body, and based on the value of the key field and the preset signal field configuration rule corresponding to each signal field to be generated, calculate the value of the corresponding signal field, and splice to obtain the initial periodic signal.

[0248] During the simulation of vehicle networking signals, it will be determined whether the current system time (i.e., the time when the signal is generated) is within the simulation time range. If so, the basic signal body will be generated according to the preset frame rate. In the above example, the start time of the current vehicle planning is 8 o'clock, and the simulated charging state begins at 8:02, and the basic signal body in the charging state begins. The end time of this charging is 8:42. At 9:02, the simulated driving state begins, and the basic signal body in the driving state begins to be generated. The end time of this trip is 9:42. If it is determined that the current system time is neither within the time range corresponding to the trip planning result nor within the time range corresponding to the charging planning result, it indicates that the vehicle is in a parking state, and the basic signal body in the parking state begins to be generated until the next trip planning begins.

[0249] The preset frame rate described in the embodiment of the present invention can be 1 second per frame, and the basic signal body will be generated at a frequency of 1 second per frame during the vehicle networking signal simulation process. Of course, the preset frame rate can also be 2 seconds per frame, 10 seconds per frame, or 30 seconds per frame. In the embodiment of the present invention, it can be determined according to actual conditions. In the embodiment of the present invention, the basic signal body can include basic information of the vehicle (such as VIN code and model, etc.) and the time when the signal is generated.

[0250] In a specific embodiment, the above-mentioned determination of the value of the key field corresponding to the basic signal body, and based on the value of the key field and the preset signal field configuration rule corresponding to each signal field to be generated, calculating the value of the corresponding signal field, and splicing to obtain the initial periodic signal, may specifically include:

[0251] Determine the value of the key field corresponding to the basic signal body, and calculate the value of the corresponding signal field based on the value of the key field and the preset signal field configuration rule corresponding to each signal field to be generated;

[0252] The basic information of the basic signal body, the value of each key field and the value of each signal field are concatenated to obtain an initial periodic signal.

[0253] In the embodiment of the present invention, the key fields in the simulated vehicle driving state are the time signal field, the speed signal field and the acceleration signal field. Further, based on the time signal field, the speed signal field, the acceleration signal field and the signal field configuration rules of the preset type corresponding to each signal field to be generated, the corresponding signal field is generated, and the basic information of the basic signal body, each key field and each signal field are spliced ​​to obtain the initial periodic signal. The time signal field is the time corresponding to the generation of the basic signal body, the speed signal field is the speed value determined when executing the simulated vehicle driving according to the speed distribution in the driving planning result, and the acceleration signal field is the acceleration value determined when executing the simulated vehicle driving according to the speed distribution in the driving planning result.

[0254] In the embodiment of the present invention, the key fields in the simulated vehicle charging state are the time signal field and the charging power signal field. Further, based on the time signal field, the charging power signal field and the signal field configuration rules of the preset type corresponding to each signal field to be generated, the corresponding signal field is generated, and the basic information of the basic signal body, each key field and each signal field are spliced ​​to obtain the initial periodic signal. The time signal field is the time corresponding to the generation of the basic signal body, the speed signal field is the speed value determined when executing the simulated vehicle driving according to the speed distribution in the driving planning result, and the charging power signal field is the charging pile power value determined when executing the simulated vehicle charging according to the random charging power in the charging planning result.

[0255] In the embodiment of the present invention, the preset signal field configuration rule corresponding to each to-be-generated signal field may be a pre-configured system logic rule, or may be a logic rule that a user reconfigures for part of the signal field based on the pre-configured system logic rule.

[0256] In a specific embodiment, as shown in Table 2, the pre-configured system logic rules used when generating the initial periodic signal may include logic rules for 6 different types of signal fields, and the simulation types of the 6 signal fields are respectively the first logic type constant, the second logic type system, the third logic type random, the fourth logic type general calculation, the fifth logic type battery cell voltage calculation, and the sixth logic type temperature probe calculation. Each signal field will perform corresponding calculations according to the configured simulation type to obtain the specific result of the signal field, where:

[0257] The signal field of the constant type is directly based on the value configured in the expression field and can be directly obtained from the database without any processing.

[0258] The signal field of the system type is the vin code. The signal generation time and other fields are output when the basic signal body is generated. No signal field configuration is performed.

[0259] The signal field of random type is simulated based on multiple parameters such as the configured resolution, coefficient, offset, maximum value, minimum value, etc. For example, the maximum value n of random is first calculated based on the following formula: n = (maximum value - minimum value) / resolution * (resolution / coefficient), and n is checked during configuration to ensure that it is an integer greater than or equal to 0. Then, an integer m is randomly generated from 0 to n; finally. The calculation result of the random signal field is: m * coefficient + offset.

[0260] For a signal field of the general calculation type, the calculation result of the signal field can be calculated through the expression configured in the expression field. If there are nested references, the priority of the calculation is controlled through the priority field.

[0261] For example, assuming that the signal field to be calculated is electric_endurance_mileage (pure electric part endurance mileage), the type of the signal field is configured as simulation_type = 4, that is, the general calculation type, the expression configured for the signal field is expression = soc_display * nominal_battery_life / 100, and the priority configured for the signal field is priority = 50. Before calculating the result of the signal field, the signal field soc_display and the signal field nominal_battery_life need to be calculated first, among which the priority of the signal field soc_display is higher than that of the signal field electric_endurance_mileage. The priority of rance_mileage, for example, the priority of the signal field soc_display is set to priority=30, the type of the signal field soc_display is configured as simulation_type=4, that is, the general calculation type, and the type of nominal_battery_life is configured as simulation_type=2, that is, the system type, which can be read directly from the database. When electric_endurance_mileage is calculated according to the priority, the values ​​of soc_display and nominal_battery_life will be substituted into the expression, and the calculation result will be assigned to electric_endurance_mileage.

[0262] The signal field of the cell voltage calculation type can use the field configured by the system field include_columns to obtain the number of cells, and calculate multiple cell voltages based on the number of cells. Each cell voltage is a signal field, and each cell voltage is quasi-normally distributed between the configured maximum and minimum values.

[0263] The signal field of the temperature probe calculation type can obtain the number of probes by using the field configured by the system field include_columns, and calculate multiple probe temperatures based on the number of probes. Each probe temperature is a signal field, and each probe temperature is normally distributed between the configured maximum and minimum values.

[0264] Table 2 Pre-configured system logic rules

[0265]

[0266]

[0267] In the embodiment of the present invention, the logic rules of hundreds of signal fields commonly used in the field of Internet of Vehicles can be configured in the database based on the above system logic rules. On the basis of the logic rules of different signal fields configured in the above database, the user can configure and modify the logic rules of the signal field, and can also add the logic rules of the signal field. For example, the expression A=B+C of the logic rule of a certain signal field configured by default in the database can be modified to A=2B-C. Therefore, in the process of Internet of Vehicles signal simulation, Internet of Vehicles signals covering different vehicle scenes can be obtained, and different types of Internet of Vehicles signals such as CAN (Vehicle Controller Area Network) simulation signals, DTC (Vehicle Diagnostic Trouble Codes) simulation signals, VHA (Vehicle Health and Alerts) simulation signals and EV (new energy vehicle signals, such as battery status, charging status, etc.) simulation signals commonly used in the field of Internet of Vehicles can be obtained.

[0268] It should be noted that, referring to the configuration rules shown in Table 2, the simulation signal name simulation_signal_name, signal name signal_name, Chinese name name_cn, expression including signal include_columns) and the creation time create_time and update time update_time in the time field cannot be configured or modified by the user.

[0269] In an optional embodiment, in the above step S106, determining whether each initial periodic signal meets the triggering condition of any preset triggering scenario may specifically include:

[0270] For each initial periodic signal, the initial periodic signal is substituted into the expression of the trigger condition of each trigger scenario matched by the vehicle;

[0271] An expression for determining whether the initial periodic signal meets the triggering condition of the triggering scenario;

[0272] If so, it is determined that the initial periodic signal triggers the corresponding triggering scenario.

[0273] In an embodiment of the present invention, the user can configure the expression of the trigger condition contained in the trigger scenario of a certain vehicle, a certain type of vehicle, or all vehicles in the database before executing the Internet of Vehicles signal simulation, or the user can configure the system default configuration in the database when developing the signal simulation program, and configure the default general trigger scenario, in which the expression of the trigger condition is set by default. The general trigger scenario can be the expression of the trigger condition configured for a certain vehicle, a certain type of vehicle, or all vehicles according to the national standard alarm commonly used in conventional technology, such as the scene configuration of the battery high temperature alarm, the braking system alarm, etc., and the user does not need to customize additional logic. The trigger scenario configured by the user can be configured according to the alarm logic customized by the user. Each trigger scenario configured by the user will be configured with the expression of the trigger condition. For example, for the low tire pressure (below 230kpa) alarm, the expression of this scenario is:

[0274] tire_pressure_right_front_value<230||tire_pressure_right_rear_inside_value<230||tire_pressure_left_rear_inside_state<230||tire_pressure_left_front_state<230

[0275] The above expressions are used to configure the low tire pressure trigger conditions of the right front wheel, right rear wheel, left rear wheel and left front wheel of the vehicle respectively.

[0276] When a frame of initial periodic signal is simulated, the expressions of the trigger conditions of all triggering scenes of the vehicle will be brought in for judgment. If the expression of the triggering condition is established (such as the tire pressure is lower than the warning value), it means that this frame of initial periodic signal has triggered the corresponding triggering scene. In this case, a copy of the initial periodic signal of this frame will be made as the basis of the event signal.

[0277] In an optional embodiment, in the above step S107, according to the rules in the triggering scenario, the signal field is reassigned based on the initial periodic signal to obtain the corresponding event signal and the adjusted periodic signal, specifically including:

[0278] For each triggering scenario triggered by the initial periodic signal, according to the signal field configuration rule of the preset type in the triggering scenario, recalculate the updated value of the corresponding signal field;

[0279] According to the signal field configuration rule of the preset type in the triggering scenario, obtain the label type of the signal field;

[0280] If the tag type of the signal field is a periodic signal, the corresponding signal field in the initial periodic signal is reassigned to the corresponding update value to obtain an adjusted periodic signal;

[0281] If the label type of the updated signal field is an event-type signal, the corresponding signal field in the copied initial periodic signal is reassigned to the corresponding updated value to obtain the event-type signal.

[0282] In each of the above triggering scenarios in the embodiment of the present invention, similar to the system logic rules pre-configured in the above driving scenario, the signal field configuration rules of different types in the triggering scenario will also be configured with reference to the system logic rules in Table 2. The pre-configured signal field configuration rules of preset types in the triggering scenario may include logic rules for 7 different types of signal fields, and the simulation types of these 7 signal fields include the above 1st to 6th logic types and the 7th logic type json array. Among them, the signal field of json array type is when multiple configured triggering scenarios are triggered at the same time, the calculation results of the signal field are calculated by the expression configured in the expression field under each triggering scenario, and the multiple calculation results are assembled in jsonarray format.

[0283] For each trigger scenario, for the signal field logically configured in the current trigger scenario, the signal field configuration rule of the preset type in the trigger scenario is used to calculate and assign the signal field to obtain the updated value corresponding to the signal field in the trigger scenario.

[0284] It should be noted that the signal field configuration rules of the preset type under the trigger scenario also configure the label type of each preset type of signal field, that is, whether the label of the signal field is a label of a periodic signal or a label of an event signal. The signal fields of the above-mentioned first to sixth analog types can be marked with the label of a periodic signal or the label of an event signal, while the signal field of the above-mentioned seventh analog type can only be marked with the label of an event signal.

[0285] Based on this, after obtaining the updated value corresponding to each signal field in the initial periodic signal, the assignment of the periodic signal and the event signal can be performed respectively according to the label of the signal field, wherein the updated value corresponding to each signal field labeled as the periodic signal is used to replace the value of each corresponding signal field in the original initial periodic signal to obtain the adjusted periodic signal under the triggering scenario; the updated value corresponding to each signal field labeled as the event signal is used to replace the value of each corresponding signal field in the copied initial periodic signal to obtain the event signal under the triggering scenario. For the signal field that has not been logically configured under the triggering scenario, the signal field will not be updated, and the value of the signal field is retained in both the adjusted periodic signal and the event signal under the triggering scenario.

[0286] In the embodiment of the present invention, the above-mentioned signal field can be represented by a json string. The above-mentioned periodic signal and event signal are json strings spliced ​​by multiple signal fields, where the key is the signal field name and the value is the calculation result.

[0287] In a specific embodiment, it is assumed that the initial periodic signal includes 5 signal fields, whose keywords (Key) are a1, a2, a3, a4 and a5 respectively, and it is assumed that the expression of the trigger condition of a certain trigger scenario 1 is a1+a2>10, and the pre-configured system logic rules in the trigger scenario configure the logic rules of the signal fields a1, a2, a3, a4 and a5, and the signal fields a1, a4 and a5 are marked with labels of periodic signals, and the signal fields a2 and a3 are marked with labels of event signals. Further, it is assumed that the value (Value) of a1 in the initial periodic signal is 8, and the value of a2 is 4, then the expression of the trigger condition of the trigger scenario is established, indicating that the initial periodic signal triggers the trigger scenario, and the initial periodic signal is first copied to obtain a copied initial periodic signal. For the signal fields a1, a2, a3, a4 and a5 that are logically configured in the initial periodic signal, the pre-configured system logic rules in the trigger scenario are used to calculate and assign values ​​to the signal fields, and the updated values ​​a1`, a2`, a3`, a4` and a5` corresponding to the signal fields in the trigger scenario are obtained. For the signal fields a1, a4 and a5 that are labeled with the labels of the periodic signal in the original initial periodic signal, the updated values ​​a1`, a4` and a5` of the signal fields are used to replace the signal fields a1, a4 and a5, and the adjusted periodic signals in the trigger scenario are a1`, a2, a3, a4` and a5`. For the signal fields a2 and a3 that are labeled with the labels of the event signal in the copied initial periodic signal, the updated values ​​a2` and a3` corresponding to the signal fields are used to replace the signal fields a2 and a3, and the event signals in the trigger scenario are a1, a2`, a3`, a4 and a5.

[0288] In the embodiment of the present invention, the signal fields of the first to sixth analog types are replacement signal fields, and the seventh signal field is a combined signal field. If the initial periodic signal triggers multiple triggering scenarios, then the tag type of the above-mentioned signal field is a periodic signal, and the corresponding signal field in the initial periodic signal is reassigned to a corresponding update value to obtain an adjusted periodic signal, specifically including:

[0289] If the initial periodic signal triggers multiple trigger scenarios, then for the signal field whose label type is a periodic signal, determine whether the signal field is updated multiple times in multiple trigger scenarios. If so, reassign the corresponding signal field in the initial periodic signal to the update value corresponding to the trigger scenario with the latest configuration time to obtain an adjusted periodic signal.

[0290] Correspondingly, if the initial periodic signal triggers multiple triggering scenarios, then if the tag type of the updated signal field is an event signal, the corresponding signal field in the copied initial periodic signal is reassigned to the corresponding updated value to obtain the event signal, specifically including:

[0291] If the initial periodic signal triggers multiple trigger scenarios, then for the signal field whose label type is an event-type signal, determine whether the signal field is updated multiple times in multiple trigger scenarios. If so, for the replacement-type signal field, the corresponding signal field in the copied initial periodic signal is reassigned to the update value corresponding to the trigger scenario with the latest configuration time. For the merged signal field, the corresponding signal field in the copied initial periodic signal is reassigned to the merged update value to obtain the event-type signal; wherein the merged update value is the value obtained by merging the update values ​​of the same merged signal field in multiple trigger scenarios.

[0292] When a frame of initial periodic signal meets multiple trigger scenarios, if the same signal field is configured in multiple trigger scenarios, the update value corresponding to the signal field of the same signal field in different trigger scenarios will be calculated, and the processing logic needs to be determined according to the type of signal field configuration. If the signal field belongs to the 1st to 6th analog types, replacement logic (that is, the processing method when the field is multiplexed) must be used for both periodic signals and event signals, that is, the update value of the signal field calculated by the logical rule configured under the trigger scenario with the latest configuration time is assigned; if the analog type of the signal field is the 7th type, for the event signal, it is necessary to calculate separately according to the logical rules of the configuration of the signal field that meet all the trigger scenarios, and merge the multiple update values ​​of the calculated signal field in multiple trigger scenarios into an array as the merged update value, and perform the assignment operation with the merged update value. Still taking the above-mentioned initial periodic signal including 5 signal fields a1, a2, a3, a4 and a5 as an example, assuming that the initial periodic signal also triggers the trigger scenario 2, and for the signal fields a1, a2, a3, a4 and a5 that are logically configured in the initial periodic signal, the pre-configured system logic rules under the trigger scenario 2 are used to calculate and assign values ​​to the signal fields, and the updated values ​​of the signal fields under the trigger scenario are a1``, a2``, a3``, a4`` and a5``. Then the adjusted periodic signal under the trigger scenario 2 is a1``, a2, a3, a4`` and a5``, and the event-type signals under the trigger scenario 2 are a1, a2``, a3``, a4 and a5. Further, assuming that the configuration time of trigger scenario 2 is later than the configuration time of the above-mentioned trigger scenario 1, the signal fields at positions 1, 4 and 5 are updated multiple times for trigger scenario 1 and trigger scenario 2. The values ​​of the signal fields a1, a4 and a5 at positions 1, 4 and 5 of the initial periodic signal are replaced with the updated values ​​a1``, a4`` and a5`` of the signal fields corresponding to the trigger scenario 2 with the latest configuration time, to obtain the adjusted periodic signals a1``, a2, a3, a4`` and a5``. Furthermore, assuming that in the signal fields a2 and a3 of the label marking the event-type signal, the analog type of the signal field a2 is 4, and the analog type of the signal field a3 is 7, then for trigger scenario 1 and trigger scenario 2, the signal fields at positions 2 and 3 are updated multiple times. For the signal field a2 at position 2, the signal field a2 at position 2 of the copied initial periodic signal is replaced with the updated value a2`` of the signal field corresponding to the trigger scenario 2 with the latest configuration time. For the signal field a3 at position 3, since it is a merged signal field, the signal field a3 at position 3 of the copied initial periodic signal is replaced with the merged updated value a3`a3``. The final updated event-type signals are a1, a2``, a3`a3``, a4 and a5.Among them, the signal field a3`a3`` is a signal field obtained by merging the signal fields a3` and a3`` at the 3rd position of trigger scene 1 and trigger scene 2.

[0293] In an optional embodiment, the method further comprises:

[0294] The obtained periodic signal is frame-drawn and output according to the preset output frequency.

[0295] In an embodiment of the present invention, in a configured driving type scenario or charging scenario, the output frequency of a periodic signal may also be configured. During the vehicle networking signal simulation process, after the periodic signal is obtained, frame extraction and output are performed according to the preset output frequency. The preset output frequency may be set according to actual conditions, for example, it may be one frame every 30 seconds. Of course, if in an embodiment of the present invention, the preset frame rate determined in the above step S105 is relatively small, for example, it is set to one frame every 30 seconds instead of one frame every 1 second, then frame extraction and output may not be performed.

[0296] The vehicle networking signal simulation method provided by the embodiment of the present invention randomly selects and calculates the number of trips and driving scenes according to the logical rules of the simulation scene configured by the user, obtains the driving planning parameters to complete the trip planning, generates the signal field according to the preset signal field configuration rules within the time range of the driving planning, obtains the initial periodic signal, and reassigns according to the triggering scene to generate the event signal and the adjusted periodic signal. The user can customize the vehicle scene to be simulated, plan different specific driving planning results in the simulation scene in a random manner, and generate the simulation signal according to the preset signal field configuration rules. The user only needs to modify the scene and signal field configuration rules to realize the signal simulation under different logics, and obtain the vehicle networking signal covering different vehicle scenes. The collected vehicle networking signal has a larger data volume and higher data quality. The user can obtain the vehicle networking signal that meets the actual needs by adjusting the scene or modifying the logic of the signal field configuration rules without modifying the code of the simulation program, thereby reducing the technical difficulty of vehicle networking signal simulation.

[0297] Embodiment 2

[0298] Based on the same inventive concept, the embodiment of the present invention also provides a vehicle networking signal simulation device, referring to Figure 2 As shown, the device may include:

[0299] The planning frequency module 101 is used to randomly select a target travel frequency scenario from at least one pre-matched travel frequency scenario according to the current date of the vehicle, and determine the vehicle planning frequency according to the target travel frequency scenario;

[0300] A driving scene selection module 102 is used to randomly select a target driving type scene from the matching driving type scenes each time according to the number of vehicle planning times;

[0301] The parameter planning module 103 is used to randomly select and calculate at least one driving configuration parameter preset in the target driving type scenario to obtain a driving planning parameter;

[0302] The itinerary planning module 104 is used to perform itinerary planning according to the driving planning parameters and obtain itinerary planning results;

[0303] The signal generation module 105 is used to generate an initial periodic signal according to the trip planning result, within the simulation time range, at a preset frame rate, and according to a preset signal field configuration rule;

[0304] A determination module 106, configured to determine whether each initial periodic signal meets a trigger condition of any preset trigger scenario;

[0305] The signal processing module 107 is used to reassign the signal field based on the initial periodic signal to obtain the corresponding event signal and the adjusted periodic signal according to the rules in each trigger scenario triggered by the initial periodic signal if the initial periodic signal meets the trigger condition of any preset trigger scenario; otherwise, the initial periodic signal is retained.

[0306] Embodiment 3

[0307] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the vehicle network signal simulation method described in the above embodiment 1 is implemented.

[0308] Embodiment 4

[0309] Based on the same inventive concept, a computer device is also provided in an embodiment of the present invention, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the vehicle network signal simulation method described in the above embodiment 1 is implemented.

[0310] Embodiment 5

[0311] Based on the same inventive concept, an embodiment of the present invention further provides a computer program product containing instructions. When the computer program product runs on a computer device, the computer device executes the vehicle network signal simulation method described in the above embodiment 1.

[0312] The principles of solving the problems by the above-mentioned apparatus, computer-readable storage medium, computer device and computer program product containing instructions in the embodiments of the present invention are similar to those of the above-mentioned methods, so their implementation can refer to the implementation of the above-mentioned methods, and the repeated parts will not be repeated.

[0313] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0314] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0315] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0316] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0317] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A vehicle networking signal simulation method, characterized in that: include: According to the current date of the vehicle, randomly select a target travel frequency scenario from at least one pre-matched travel frequency scenario, and determine the vehicle planning frequency according to the target travel frequency scenario; According to the number of vehicle planning, a target driving type scenario is randomly selected from the matching driving type scenarios each time; Randomly selecting and calculating at least one driving configuration parameter preset in the target driving type scenario to obtain a driving planning parameter; Perform itinerary planning according to the driving planning parameters to obtain itinerary planning results; According to the trip planning result, within the simulation time range, at a preset frame rate, and according to a preset signal field configuration rule, an initial periodic signal is generated; Determining whether each of the initial periodic signals meets the triggering condition of any preset triggering scenario; If so, according to the rules in the trigger scenario, reassign the signal field based on the initial periodic signal to obtain the corresponding event signal and the adjusted periodic signal; Otherwise, the initial periodic signal is retained.

2. The method according to claim 1, characterized in that The generating of the initial periodic signal according to the trip planning result, within the simulation time range, at a preset frame rate, and according to a preset signal field configuration rule includes: According to the trip planning result, generating a basic signal body at a preset frame rate within the simulation time range; Determine the value of the key field corresponding to the basic signal body, and based on the value of the key field and the preset signal field configuration rule corresponding to each signal field to be generated, calculate the value of the corresponding signal field, and splice to obtain the initial periodic signal.

3. The method according to claim 2, characterized in that The determining the value of the key field corresponding to the basic signal body, and calculating the value of the corresponding signal field based on the value of the key field and the preset signal field configuration rule corresponding to each signal field to be generated, and splicing to obtain the initial periodic signal, includes: Determine the value of the key field corresponding to the basic signal body, and calculate the value of the corresponding signal field based on the value of the key field and the preset signal field configuration rule corresponding to each signal field to be generated; The basic information of the basic signal body, the value of each key field and the value of each signal field are concatenated to obtain an initial periodic signal.

4. The method according to claim 2, characterized in that: The reassigning of signal fields based on the initial periodic signal according to the rules in the triggering scenario to obtain the corresponding event signal and the adjusted periodic signal includes: For each triggering scenario triggered by the initial periodic signal, recalculate the updated value of the corresponding signal field according to the signal field configuration rule of the preset type in the triggering scenario; Acquire a tag type of the signal field according to a signal field configuration rule of a preset type in the triggering scenario; If the tag type of the signal field is a periodic signal, reassigning the corresponding signal field in the initial periodic signal to a corresponding updated value to obtain an adjusted periodic signal; If the tag type of the updated signal field is an event-type signal, the corresponding signal field in the copied initial periodic signal is reassigned to a corresponding updated value to obtain an event-type signal.

5. The method according to claim 4, characterized in that If the tag type of the signal field is a periodic signal, reassigning the corresponding signal field in the initial periodic signal to a corresponding updated value to obtain an adjusted periodic signal, including: If the initial periodic signal triggers multiple trigger scenarios, then for the signal field whose tag type is a periodic signal, determine whether the signal field is updated multiple times in the multiple trigger scenarios. If so, reassign the corresponding signal field in the initial periodic signal to the update value corresponding to the trigger scenario with the latest configuration time to obtain the adjusted periodic signal.

6. The method according to claim 4, characterized in that If the tag type of the updated signal field is an event type signal, reassigning the corresponding signal field in the copied initial periodic signal to a corresponding updated value to obtain the event type signal, including: If the initial periodic signal triggers multiple trigger scenarios, then for the signal field whose label type is an event-type signal, determine whether the signal field is updated multiple times in the multiple trigger scenarios. If so, for the replacement-type signal field, the corresponding signal field in the copied initial periodic signal is reassigned to the update value corresponding to the trigger scenario with the latest configuration time. For the merged signal field, the corresponding signal field in the copied initial periodic signal is reassigned to the merged update value to obtain the event-type signal; wherein the merged update value is the value obtained by merging the update values ​​of the same merged signal field under the multiple trigger scenarios.

7. The method according to claim 1, characterized in that The method of randomly selecting a target travel frequency scenario from at least one pre-matched travel frequency scenario according to the current date of the vehicle, and determining the vehicle planning frequency according to the target travel frequency scenario includes: Determine at least one trip frequency scenario that matches the vehicle, and randomly select a trip frequency scenario corresponding to the current date as the target trip frequency scenario according to the weight according to the date range in the at least one trip frequency scenario; the trip frequency scenario is a scenario configured by the user or set by default that includes a date range and a preset trip frequency range, and each of the trip frequency scenarios is configured with a corresponding weight; The planned vehicle number is obtained by randomly selecting from a preset travel number range in the target travel number scenario.

8. The method according to any one of claims 1 to 7, characterized in that: The step of randomly selecting a target driving type scene from the matching driving type scenes each time according to the number of vehicle planning times includes: If the number of vehicle planning times is not zero, determining whether the number of actual vehicle planning times reaches the number of vehicle planning times; If not, at least one driving type scenario matching the vehicle is determined, and according to the date range in the at least one driving type scenario, a driving type scenario corresponding to the current date is randomly selected according to the weight as the target driving type scenario; the driving type scenario is a user-configured or defaulted scenario containing a date range and at least one preset driving configuration parameter, and each driving type scenario is configured with a corresponding weight; If yes, end the vehicle networking signal simulation process within the current date.

9. The method according to claim 8, characterized in that The preset driving configuration parameters include at least one of driving mileage, average speed, driving time, remaining power difference and driving road conditions; The step of randomly selecting and calculating at least one driving configuration parameter preset in the target driving type scenario to obtain a driving planning parameter includes: If the preset driving configuration parameters include driving road conditions, then obtaining planned road condition information of the driving road conditions; If the preset driving configuration parameters include an average speed, a random selection is performed according to the parameter range of the average speed to obtain a planned average speed; If the preset driving configuration parameters include driving road conditions but do not include average speed, a reference average speed is calculated based on the planned road condition information; If the preset driving configuration parameters do not include driving road conditions and average speed, randomly selecting road condition information according to pre-configured basic road condition information to obtain reference road condition information, and calculating a reference average speed according to the reference road condition information; If the parameter ranges of the mileage and the driving duration are not empty, randomly selecting from the parameter ranges of the mileage and the driving duration to obtain the planned mileage and the planned driving duration; If the parameter ranges of the mileage and the driving duration are empty, and the parameter range of the remaining power difference is not empty, then according to the parameter range of the remaining power difference, a planned remaining power difference is randomly selected, and the planned mileage is calculated according to the planned remaining power difference, and the planned driving duration is calculated in combination with the planned average speed or the reference average speed; If the parameter ranges of the mileage, the remaining power difference and the driving duration are all empty, one of the planned mileage and the planned driving duration is randomly selected within the default parameter value range, and the other of the planned mileage and the planned driving duration is calculated in combination with the planned average speed or the reference average speed; If the parameter range of the driving mileage is empty, and the parameter ranges of the driving duration and the remaining power difference are not empty, then according to the parameter range of the remaining power difference, a planned remaining power difference is randomly selected, and the planned driving mileage is calculated according to the planned remaining power difference, and the planned driving duration is randomly selected within the parameter range of the driving duration; If the parameter range of the difference between the mileage and the remaining power is empty, and the parameter range of the driving duration is not empty, the planned driving duration is randomly selected within the parameter range of the driving duration, and the planned mileage is calculated in combination with the planned average speed or the reference average speed.

10. The method according to claim 9, characterized in that The reference road condition information includes the time proportion of each road condition in the driving road condition; The road condition information is randomly selected according to the pre-configured basic road condition information to obtain reference road condition information, which specifically includes: According to various road conditions in the pre-configured basic road conditions, the time proportion of each road condition is randomly selected within a preset time proportion enumeration value range to obtain the reference road condition information.

11. The method according to claim 9, characterized in that The performing itinerary planning according to the driving planning parameters to obtain the itinerary planning result includes: performing driving planning according to the driving planning parameters to obtain a driving planning result; Determining the amount of power to be consumed by the vehicle according to the driving planning result; If the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than a preset power threshold, determining a charging planning result according to the remaining power of the vehicle; A parking planning result is determined according to the driving planning result and / or the charging planning result.

12. The method according to claim 11, characterized in that The performing driving planning according to the driving planning parameters to obtain a driving planning result includes: If the driving planning parameters include the planned average speed, then within the planned driving mileage and planned driving duration, speed distribution is performed according to the planned average speed and a preset acceleration range, and the speed and acceleration at each moment are determined to obtain the driving planning result; If the driving planning parameters do not include a planned average speed, determining the road condition time length for each road condition according to the planned road condition information or the reference road condition information; Dividing the road condition time length of each road condition into multiple time segments according to the preset time segment length, and obtaining the speed range and acceleration range in each time segment; After randomly combining the multiple duration segments and adding a termination duration segment, speed distribution is performed according to the speed range and acceleration range in each duration segment, the speed and acceleration at each moment are determined, and the driving planning result is obtained; the speed of the termination duration segment is reduced from the speed range of the adjacent time period to zero.

13. The method according to claim 12, characterized in that In the process of performing speed distribution according to the combined driving duration, the method further includes: According to the speed range of two adjacent time segments, determining whether the speeds of the two adjacent time segments jump; If so, the speed is adjusted according to the preset acceleration value based on the speed value at the last moment of the previous duration segment from the start moment of the next duration segment until the adjusted speed falls into the speed range corresponding to the next duration segment.

14. The method according to claim 13, characterized in that The planned road condition information or the reference road condition information includes a time proportion of each road condition; Determining the road condition time length of each road condition according to the planned road condition information or the reference road condition information specifically includes: According to the time proportion of each road condition, the time proportion of all road conditions and the planned driving time, the road condition time length of each road condition is obtained.

15. The method according to claim 13, characterized in that The method of dividing the road condition time length of each road condition into a plurality of time segments according to the preset time segment length, and obtaining the speed range and acceleration range within each time segment, includes: For each road condition including a continuous duration, the road condition time length is subtracted from the corresponding continuous duration to obtain a remaining time length of the road condition, and the remaining time length of the road condition is divided into a plurality of duration segments according to a preset time segment length to obtain a speed range and an acceleration range within each duration segment; the planned road condition information or the reference road condition information also includes a continuous duration of at least one road condition; For each road condition that does not include a continuous duration, the road condition time length is divided into a plurality of duration segments according to a preset time segment length, and a speed range and an acceleration range within each duration segment are obtained.

16. The method according to claim 15, characterized in that After randomly combining the multiple duration segments and adding a termination duration segment, speed distribution is performed according to the speed range and acceleration range in each duration segment, and the speed and acceleration at each moment are determined to obtain a driving planning result, including: Randomly combine each of the continuous durations and all the duration segments and add a termination duration segment to obtain a combined driving duration; Speed ​​distribution is performed according to the speed range and acceleration range in each continuous duration and each duration segment in the combined driving duration, the speed and acceleration at each moment are determined, and the driving planning result is obtained.

17. The method according to claim 11, characterized in that If the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than a preset power threshold, determining a charging planning result according to the remaining power of the vehicle includes: If the difference between the remaining power of the vehicle and the power to be consumed by the vehicle is less than a preset power threshold, a charging pile power value is randomly selected from a preset charging pile power enumeration value range; A charging power target value is randomly selected from the charging target power range, and the charging time is determined according to the charging pile power value and the remaining power of the vehicle to obtain a charging planning result.

18. The method according to claim 17, characterized in that The simulation time range is determined by: The simulation time range is determined according to the start time of the current vehicle planning, the time range corresponding to the charging planning result and / or the time range corresponding to the driving planning result; wherein: The time range corresponding to the charging planning result is determined by the following method: Randomly select a waiting time for entering the charging state within the waiting time range for starting the charging state; According to the start time of the current vehicle plan, the waiting charging time and the planned charging time, the time range corresponding to the charging plan result is obtained; The time range corresponding to the driving planning result is determined by: If the trip planning result does not include a charging planning result, a waiting driving time for entering the driving state is randomly selected within the waiting time range for starting the driving state; According to the start time of the current vehicle planning, the waiting driving time and the planned driving time, the time range corresponding to the driving planning result is obtained; If the trip planning result includes a charging planning result, a waiting driving time for entering the driving state is randomly selected within the waiting time range for starting the driving state; According to the start time of the current vehicle planning, the waiting charging time, the planned charging time, the waiting driving time and the planned driving time, the time range corresponding to the driving planning result is obtained.

19. The method according to claim 18, characterized in that Also includes: Determine whether the current time is within the time range corresponding to the driving planning result or the time range corresponding to the charging planning result; If not, it is determined that the current time is within the time range corresponding to the parking planning result.

20. The method according to claim 1, characterized in that Also includes: The periodic signal obtained within the current date is frame-extracted and output according to the preset output frequency.

21. A device for simulating vehicle network signals, characterized in that: include: A planning frequency module, used to randomly select a target travel frequency scenario from at least one pre-matched travel frequency scenario according to the current date of the vehicle, and determine the vehicle planning frequency according to the target travel frequency scenario; A driving scene selection module, used for randomly selecting a target driving type scene from the matching driving type scenes each time according to the number of vehicle planning times; A parameter planning module, used to randomly select and calculate at least one driving configuration parameter preset in the target driving type scenario to obtain a driving planning parameter; A trip planning module, used to perform trip planning according to the driving planning parameters and obtain a trip planning result; A signal generation module, used to generate an initial periodic signal according to the trip planning result, within a simulation time range, at a preset frame rate, and according to a preset signal field configuration rule; A judging module, used for judging whether each of the initial periodic signals meets the triggering condition of any preset triggering scenario; A signal processing module, configured to re-assign signal fields based on the initial periodic signal according to the rules in each triggering scenario triggered by the initial periodic signal if the initial periodic signal meets the triggering condition of any preset triggering scenario, so as to obtain a corresponding event-type signal and an adjusted periodic signal; Otherwise, the initial periodic signal is retained.

22. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle network signal simulation method as described in any one of claims 1 to 20 is implemented.

23. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the vehicle network signal simulation method as described in any one of claims 1-20.

24. A computer program product comprising instructions, which, when executed on a computer device, enables the computer device to execute the vehicle networking signal simulation method as described in any one of claims 1 to 20.

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