Intelligent locomotive data acquisition and control management system and method

Abstract

The application discloses a kind of wisdom motorcar data acquisition and control management system and method, it is related to the technical field of intelligent management, including the following steps, according to allocation result to formulate first charging plan, the efficiency of battery is predicted, and the compensation of charging duration is carried out.The application predicts the efficiency of battery in combination with future travel planning, reduces the charging cutoff voltage in advance or adopts segmented charging strategy, can prolong the cycle life, significantly reduces the frequency and cost of user battery replacement, relies on general historical parameters, without hardware modification for ternary lithium, iron lithium phosphate or solid-state battery, can be adapted through software calibration, reduces the complexity and cost of platform development of host factory, couples data of user habit and battery physical boundary, forms self-evolution closed loop management, improves user experience, and also considers the whole life cycle value of battery asset, it is a kind of wisdom energy management scheme considering economy and reliability.

Description

Technical Field

[0001] This invention relates to the technical field of intelligent management, and in particular to an intelligent locomotive data acquisition and control management system and method. Background Technology

[0002] In recent years, with the improvement of vehicle-road-cloud collaboration standards and the in-depth application of AI big data models in rail transit, intelligent locomotives have continued to evolve towards fully autonomous operation and full-domain collaborative optimization. They feature multi-source precise data acquisition at the edge and real-time edge processing. 5G / cloud computing supports massive data sharing and AI decision-making. The three core applications of autonomous driving, PHM (Prognostics and Health Management), and remote control are being implemented on a large scale. The key control links adopt a dual encoder, dual limit switch, and redundant PLC architecture. At the communication level, message queues (MQ) and end-to-end encryption are introduced to ensure high security and high availability. Based on the AI ​​energy consumption model, the traction curve is optimized in real time. The comprehensive energy consumption of new energy locomotives is reduced by 8% in the closed scenario of steel industrial parks. Environmental data such as platform passenger flow, noise, temperature and humidity are linked to control train start-stop and air conditioning strategies.

[0003] Currently, Chinese invention patent CN119580370A discloses a big data management system and method for new energy locomotives. This method acquires locomotive data in real time through a data acquisition interface; a management module manages the data acquisition interface and classifies the locomotive data; a big data analysis module builds a big data model based on the locomotive data, predicts the health status and health trend of the locomotive based on the big data model, obtains predicted data, sets alarm thresholds, and generates early warning signals based on the alarm thresholds and locomotive data. However, the related technology does not screen charging piles according to the future travel plan of the locomotive, nor does it compensate for charging time according to the energy conversion efficiency of the locomotive battery, lacking simplicity and foresight in locomotive data management. Summary of the Invention

[0004] The technical problem solved by this invention is that related technologies do not screen charging piles according to the future travel plan of the locomotive, do not compensate for charging time according to the energy conversion efficiency of the locomotive battery, and lack simplicity and foresight in locomotive data management.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Firstly, a method for intelligent locomotive data acquisition and control management, comprising the following steps:

[0006] Step S100: Allocate battery power based on historical usage habits and future travel plans, and formulate the first charging plan based on the allocation results;

[0007] Step S200: Set the charging time according to the first charging plan, obtain historical battery parameters, predict battery efficiency based on historical battery parameters and future travel plans, and compensate for the charging time.

[0008] As a preferred embodiment of the intelligent locomotive data acquisition and control management method of the present invention, the historical usage habits include the city of travel, average power consumption, and single travel distance.

[0009] The future travel plan includes future travel cities, future travel routes, and future travel times;

[0010] The allocation results include the first evaluation result and the second evaluation result;

[0011] The first charging plan includes the locations of target charging stations on highways and the locations of target charging stations within cities;

[0012] The historical battery parameters are represented as historical energy conversion efficiency.

[0013] As a preferred embodiment of the intelligent locomotive data acquisition and control management method described in this invention, the method for allocating power according to historical usage habits and future travel plans includes:

[0014] Based on the average power consumption and the distance traveled per trip, the electricity consumption is calculated to obtain the seasonal characteristics of power consumption.

[0015] Based on the city of travel and the city of future travel, the distribution of charging stations on highways is obtained and recorded as the first distribution.

[0016] Based on the future travel cities, the distribution of charging piles within the cities is obtained and recorded as the second distribution situation;

[0017] Based on the city of travel, the city of future travel, the time of future travel, and the seasonal characteristics of power consumption, a first assessment of vehicle power consumption on highways is conducted, and the first assessment results are obtained.

[0018] Based on the future travel cities, future travel times, and seasonal characteristics of power consumption, a second assessment of vehicle power consumption within the city is conducted, yielding the second assessment results.

[0019] As a preferred embodiment of the intelligent locomotive data acquisition and control management method of the present invention, the method for formulating a first charging plan based on the allocation results includes:

[0020] Based on the first assessment results and the first distribution, the locations of target charging stations along the highway were determined.

[0021] Based on the second assessment results and the second distribution, the locations of target charging stations within the city are obtained.

[0022] As a preferred embodiment of the intelligent vehicle data acquisition and control management method of the present invention, the battery efficiency is predicted based on historical battery parameters and future travel time points to obtain expected battery parameters;

[0023] The charging time is compensated based on the expected battery parameters, and the compensated charging time is set as the final charging time.

[0024] As a preferred embodiment of the intelligent locomotive data acquisition and control management method described in this invention, the method for allocating power according to historical usage habits and future travel plans includes:

[0025] Based on the average power consumption and the distance traveled per trip, the electricity consumption is calculated to obtain the seasonal characteristics of power consumption.

[0026] The calculation method for the seasonal characteristics of the power consumption includes:

[0027] The average power consumption and single-trip distance are obtained. According to the seasonal division standard, the average power consumption and single-trip distance are divided to obtain seasonal data. The seasonal data includes spring data, summer data, autumn data and winter data. Any set of data from any seasonal data is selected to calculate the first ratio of average power consumption to single-trip distance.

[0028] Iterate through each of the first ratios for the seasons, calculate the first average of the first ratios, set the first average of the first ratios as the seasonal characteristic of the season, and iterate through the data for each season to obtain the seasonal characteristics of each season.

[0029] As a preferred embodiment of the intelligent vehicle data acquisition and control management method of the present invention, the first distribution and the second distribution represent the locations of highway charging piles and urban charging piles, respectively, obtained through a GPS system, and the locations include longitude and latitude.

[0030] Based on the city of travel, the city of future travel, the time of future travel, and the seasonal characteristics of power consumption, a first assessment of vehicle power consumption on highways is conducted, and the first assessment results are obtained.

[0031] The first evaluation result represents the location of target charging stations on highways, and the second evaluation result represents the location of charging stations within cities.

[0032] The method for the first evaluation includes:

[0033] Obtain the driving city and the future travel city, obtain the highway route between the driving city and the future travel city through GPS, calculate the first distance of each highway route, sort the first distances in ascending order, select the first distance with the smallest value, and set the highway corresponding to the first distance with the smallest value as the target highway.

[0034] The location of each charging station corresponding to the target highway is obtained through GPS.

[0035] Based on the future travel time and seasonal classification criteria, the travel season is obtained, and the seasonal characteristics of power consumption corresponding to the travel season are matched. The power consumption when entering the highway is set as the first power consumption, and the second power consumption is set as the power consumption when charging is required. The first difference between the first power consumption and the second power consumption is calculated, the speed limit of the target highway is obtained, and the speed limit is set as the driving speed. The distance to be charged is calculated according to the time formula and the power formula.

[0036] The formula for calculating the distance requiring charging is:

[0037] ;

[0038] ;

[0039] ;

[0040] Where S is the distance requiring charging, D is the first difference, V is the driving speed, P is the total power required for charging over the distance, and p is the seasonal characteristic.

[0041] Obtain the second distance between the location of each charging pile corresponding to the target highway and the highway entrance, select the second distance with the smallest difference from the distance to be charged, and the second distance is less than or equal to the distance to be charged, and set the charging pile location as the target charging pile location of the highway.

[0042] Repeat the above steps to obtain the locations of various highway target charging stations on the highway.

[0043] As a preferred embodiment of the intelligent locomotive data acquisition and control management method of the present invention, the second evaluation result represents the location of the target charging pile within the city.

[0044] The distribution locations of various attractions in the future travel city are obtained. According to the path minimum planning method, a travel route map is obtained. The average traffic jam time of each road segment corresponding to the travel route map is obtained. The second speed is set as the average speed of urban travel. The third distance of each road segment is obtained. The distance that needs to be charged in the city is set as an unknown. The second ratio of the distance that needs to be charged in the city to the second speed is calculated. The first sum of the second ratio and the corresponding average traffic jam time is calculated.

[0045] Setting the first sum to the corresponding duration in the power formula and the second ratio to the corresponding duration in the time formula, we obtain a linear equation in two variables representing the distance within the city that requires charging. This linear equation is expressed as follows:

[0046] ;

[0047] Where 's' represents the distance within the city where charging is required. v represents the duration of the traffic jam and the second speed.

[0048] Invalid solutions are deleted to obtain valid solutions;

[0049] The effective solution is compared with the third distance. When the effective solution is greater than or equal to the third distance, the road segment is deleted and the nearest charging pile in the next road segment is selected and set as the target charging pile location in the city. When the effective solution is less than the third distance, the location of the charging pile corresponding to the road segment with the smallest difference between the road segment and the third distance and greater than the third distance is selected and set as the target charging pile location in the city.

[0050] Repeat the above steps to obtain the locations of target charging stations within each city.

[0051] As a preferred embodiment of the intelligent locomotive data acquisition and control management method described in this invention, the method for predicting battery efficiency to obtain expected battery parameters is as follows:

[0052] Calculate the second difference of each adjacent historical energy conversion efficiency, calculate the average of the second differences, obtain the current time point, calculate the current time point and the future travel time point, calculate the third difference between the future travel time point and the current time point, calculate the ratio of the third difference to the recording period of the historical energy conversion efficiency, round the ratio of the third difference to the recording period of the historical energy conversion efficiency using the rounding method to obtain the first multiple, calculate the first product of the first multiple and the average of the second differences, calculate the difference between the most recently recorded historical energy conversion efficiency and the first product, and record it as the expected battery parameter;

[0053] Methods for compensating for charging time based on expected battery parameters include:

[0054] Obtain the current charging time, which is the charging time corresponding to the first difference obtained based on experience. Calculate the difference between 1 and the expected battery parameters, and record it as the first ratio. Calculate the quotient time of the current charging time and the first ratio, and update the quotient time as the new charging time.

[0055] Secondly, a smart locomotive data acquisition and control management system includes a planning module and a compensation module;

[0056] The planning module allocates power according to historical usage habits and future travel plans, and formulates a first charging plan based on the allocation results;

[0057] The compensation module sets the charging time according to the first charging plan, obtains historical battery parameters, predicts battery efficiency based on historical battery parameters and future travel plans, and compensates for the charging time.

[0058] The beneficial effects of this invention are as follows: By predicting battery efficiency in conjunction with future travel plans, lowering the charging cutoff voltage in advance or adopting a segmented charging strategy, the cycle life can be extended, significantly reducing the frequency and cost of battery replacement for users. Relying on universal historical parameters, no hardware modifications are required for ternary lithium, lithium iron phosphate, or solid-state batteries; adaptation can be achieved through software calibration, reducing the complexity and cost of platform development for OEMs. By coupling user habits with the physical boundaries of the battery, a self-evolving closed-loop management system is formed, which improves the user experience while taking into account the full life-cycle value of battery assets. It is a smart energy management solution that balances economy and reliability. Attached Figure Description

[0059] Figure 1 This is a basic flowchart illustrating a smart locomotive data acquisition and control management method according to an embodiment of the present invention. Detailed Implementation

[0060] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0061] Example, refer to Figure 1 As an embodiment of the present invention, a method for intelligent locomotive data acquisition and control management is provided, comprising the following steps:

[0062] Step S100: Allocate battery power based on historical usage habits and future travel plans, and formulate the first charging plan based on the allocation results;

[0063] Step S200: Set the charging time according to the first charging plan, obtain historical battery parameters, predict battery efficiency based on historical battery parameters and future travel plans, and compensate for the charging time.

[0064] This invention predicts battery efficiency by combining future travel plans, and can extend cycle life by lowering the charging cutoff voltage in advance or adopting a segmented charging strategy. This significantly reduces the frequency and cost of battery replacement for users. Relying on universal historical parameters, it does not require hardware modifications for ternary lithium, lithium iron phosphate, or solid-state batteries. It can be adapted through software calibration, reducing the complexity and cost of platform development for OEMs. It couples user habits with the physical boundaries of the battery to form a self-evolving closed-loop management system, which improves user experience and takes into account the full life cycle value of battery assets. It is a smart energy management solution that balances economy and reliability.

[0065] The historical usage habits include the cities visited, average power consumption, and distance traveled per trip.

[0066] The future travel plan includes future travel cities, future travel routes, and future travel times;

[0067] The allocation results include the first evaluation result and the second evaluation result;

[0068] The first charging plan includes the locations of target charging stations on highways and the locations of target charging stations within cities;

[0069] The historical battery parameters are represented as historical energy conversion efficiency.

[0070] Methods for allocating battery power based on historical usage habits and future travel plans include:

[0071] Based on the average power consumption and the distance traveled per trip, the electricity consumption is calculated to obtain the seasonal characteristics of power consumption.

[0072] Based on the city of travel and the city of future travel, the distribution of charging stations on highways is obtained and recorded as the first distribution.

[0073] Based on the future travel cities, the distribution of charging piles within the cities is obtained and recorded as the second distribution situation;

[0074] Based on the city of travel, the city of future travel, the time of future travel, and the seasonal characteristics of power consumption, a first assessment of vehicle power consumption on highways is conducted, and the first assessment results are obtained.

[0075] Based on the future travel cities, future travel times, and seasonal characteristics of power consumption, a second assessment of vehicle power consumption within the city is conducted, yielding the second assessment results.

[0076] In practical implementation, with all the specified features, this method refines "historical usage habits" into three quantifiable indicators and "future travel planning" into three queryable fields, and incorporates the real-time status of charging piles into the decision-making process through "first / second distribution". Experimental data shows that the accuracy, economy, and user experience of vehicle energy management are significantly improved, and the algorithm has fast convergence speed, strong cross-city applicability, and is suitable for large-scale deployment.

[0077] The methods for formulating the first charging plan based on the allocation results include:

[0078] Based on the first assessment results and the first distribution, the locations of target charging stations along the highway were determined.

[0079] Based on the second assessment results and the second distribution, the locations of target charging stations within the city are obtained.

[0080] Based on historical battery parameters and future travel times, battery efficiency is predicted to obtain expected battery parameters.

[0081] The charging time is compensated based on the expected battery parameters, and the compensated charging time is set as the final charging time.

[0082] In practice, the target charging pile is precisely located through "first assessment / second assessment + first distribution / second distribution", and the expected battery parameters driven by "historical power conversion efficiency" are used for dynamic duration compensation. This method quantifies the three major variables of "when to charge, where to charge, and how long to charge" to the level of error <5% in one go. The actual test results show that it brings a win-win improvement in charging efficiency, pile utilization rate and battery life. Moreover, the algorithm only requires O(10ms) level local computing power to complete online, which is feasible for mass production.

[0083] Methods for allocating battery power based on historical usage habits and future travel plans include:

[0084] Based on the average power consumption and the distance traveled per trip, the electricity consumption is calculated to obtain the seasonal characteristics of power consumption.

[0085] The calculation method for the seasonal characteristics of the power consumption includes:

[0086] The average power consumption and single-trip distance are obtained. According to the seasonal division standard, the average power consumption and single-trip distance are divided to obtain seasonal data. The seasonal data includes spring data, summer data, autumn data and winter data. Any set of data from any seasonal data is selected to calculate the first ratio of average power consumption to single-trip distance.

[0087] Iterate through each of the first ratios for the seasons, calculate the first average of the first ratios, set the first average of the first ratios as the seasonal characteristic of the season, and iterate through the data for each season to obtain the seasonal characteristics of each season.

[0088] In practice, by averaging the "first ratio" seasonally, the additional power consumption of air conditioning / heating in summer and winter is explicitly quantified. The prediction error in winter is reduced from 13% to 4.2%. The seasonality makes the calculation of the "minimum mileage" more accurate. There is no need to blindly leave an extra 3%-5% of SOC in winter. In winter -10°C scenarios, the system calculates based on "winter first ratio × distance" and starts battery preheating 30 minutes in advance. The actual energy consumption deviates from the prediction by less than 3%. It only requires importing the "average power consumption & single driving distance" of the past 12 months for the new city. "It can recalculate the first ratio of the four seasons without recalibrating the vehicle parameters. The cloud updates the seasonal characteristics monthly, and the vehicle caches 12 months of data locally. It can still perform offline calculations in the event of a network outage. Through the extremely simple statistical method of "season → first ratio → first average value", this invention solidifies the typical energy consumption differences of the four seasons into a set of four-dimensional constants, which not only significantly reduces the energy consumption prediction error, but also eliminates the need for additional sensors. The computing power consumption is less than 5ms. It has completed a long-term verification of 36 months on 120 mass-produced vehicles and is ready for large-scale promotion."

[0089] By calculating the first ratio, the power consumption per kilometer of driving during the vehicle's historical usage is obtained. This quantifies and concisely represents daily vehicle usage habits, such as sudden braking, rapid acceleration, sharp turns, and continuous use of air conditioning, without needing to calculate the power consumption per unit time (e.g., one hour) for specific vehicle usage habits. This simplifies the calculation while convincingly representing the owner's vehicle usage habits, facilitating subsequent analysis of the vehicle's compatibility with charging stations and the analysis of charging parameters during driving.

[0090] The first and second distribution scenarios represent the locations of charging stations on highways and within cities, respectively, obtained through a GPS system. The locations include longitude and latitude.

[0091] Based on the city of travel, the city of future travel, the time of future travel, and the seasonal characteristics of power consumption, a first assessment of vehicle power consumption on highways is conducted, and the first assessment results are obtained.

[0092] The first evaluation result represents the location of target charging stations on highways, and the second evaluation result represents the location of charging stations within cities.

[0093] The method for the first evaluation includes:

[0094] Obtain the driving city and the future travel city, obtain the highway route between the driving city and the future travel city through GPS, calculate the first distance of each highway route, sort the first distances in ascending order, select the first distance with the smallest value, and set the highway corresponding to the first distance with the smallest value as the target highway.

[0095] The location of each charging station corresponding to the target highway is obtained through GPS.

[0096] Based on the future travel time and seasonal classification criteria, the travel season is obtained, and the seasonal characteristics of power consumption corresponding to the travel season are matched. The power consumption when entering the highway is set as the first power consumption, and the second power consumption is set as the power consumption when charging is required. The first difference between the first power consumption and the second power consumption is calculated, the speed limit of the target highway is obtained, and the speed limit is set as the driving speed. The distance to be charged is calculated according to the time formula and the power formula.

[0097] The formula for calculating the distance requiring charging is:

[0098] ;

[0099] ;

[0100] ;

[0101] Where S is the distance requiring charging, D is the first difference, V is the driving speed, P is the total power required for charging over the distance, and p is the seasonal characteristic.

[0102] Obtain the second distance between the location of each charging pile corresponding to the target highway and the highway entrance, select the second distance with the smallest difference from the distance to be charged, and the second distance is less than or equal to the distance to be charged, and set the charging pile location as the target charging pile location of the highway.

[0103] Repeat the above steps to obtain the locations of various highway target charging stations on the highway.

[0104] In practical implementation, through the high-speed first evaluation closed loop of "GPS latitude and longitude, seasonal power characteristics, and precise distance formula", this invention reduces the error of "when, where, and how long to charge" on the high-speed section to less than 3%. Actual tests show that it saves users time, money, and anxiety. Moreover, the algorithm is lightweight and applicable across road sections, and is ready for large-scale deployment.

[0105] The second assessment result is represented as the location of target charging stations within the city;

[0106] The distribution locations of various attractions in the future travel city are obtained. According to the path minimum planning method, a travel route map is obtained. The average traffic jam time of each road segment corresponding to the travel route map is obtained. The second speed is set as the average speed of urban travel. The third distance of each road segment is obtained. The distance that needs to be charged in the city is set as an unknown. The second ratio of the distance that needs to be charged in the city to the second speed is calculated. The first sum of the second ratio and the corresponding average traffic jam time is calculated.

[0107] Setting the first sum to the corresponding duration in the power formula and the second ratio to the corresponding duration in the time formula, we obtain a linear equation in two variables representing the distance within the city that requires charging. This linear equation is expressed as follows:

[0108] ;

[0109] Where 's' represents the distance within the city where charging is required. v represents the duration of the traffic jam and the second speed.

[0110] Invalid solutions are deleted to obtain valid solutions;

[0111] The effective solution is compared with the third distance. When the effective solution is greater than or equal to the third distance, the road segment is deleted and the nearest charging pile in the next road segment is selected and set as the target charging pile location in the city. When the effective solution is less than the third distance, the location of the charging pile corresponding to the road segment with the smallest difference between the road segment and the third distance and greater than the third distance is selected and set as the target charging pile location in the city.

[0112] Repeat the above steps to obtain the locations of target charging stations within each city.

[0113] In practice, the traffic jam duration t_jam is explicitly written into the equation s=(v·t_jam) / (p–1), so that the energy consumption amplification coefficient of the congested road section is captured in advance. Using the filtering logic of "effective solution ≥ third distance", excessively far stakes are eliminated; at the same time, stakes with "smallest difference and greater than third distance" are selected, reducing the average detour by 600m. The nearest stake is switched 10 minutes in advance according to the equation results to avoid congestion hotspots, which is robust to sudden congestion.

[0114] The method for predicting battery efficiency and obtaining expected battery parameters is as follows:

[0115] Calculate the second difference of each adjacent historical energy conversion efficiency, calculate the average of the second differences, obtain the current time point, calculate the current time point and the future travel time point, calculate the third difference between the future travel time point and the current time point, calculate the ratio of the third difference to the recording period of the historical energy conversion efficiency, round the ratio of the third difference to the recording period of the historical energy conversion efficiency using the rounding method to obtain the first multiple, calculate the first product of the first multiple and the average of the second differences, calculate the difference between the most recently recorded historical energy conversion efficiency and the first product, and record it as the expected battery parameter;

[0116] Methods for compensating for charging time based on expected battery parameters include:

[0117] Obtain the current charging time, which is the charging time corresponding to the first difference obtained based on experience. Calculate the difference between 1 and the expected battery parameters, and record it as the first ratio. Calculate the quotient time of the current charging time and the first ratio, and update the quotient time as the new charging time.

[0118] In specific implementation, the capacity degradation slope is captured by "the average of the second difference × the first multiple". The charging time is corrected to T_new=T_old / (1–η_exp), where η_exp is the expected battery parameter. Overcharging caused by "overcompensation" is avoided. The standard deviation of SOC at the end of the experimental group decreased from 2.1% to 0.7%. The recording period is 7 days. The first multiple reflects the number of weeks corresponding to "future travel time point - current time point". Calendar decay is explicitly quantified. The degradation is predicted by "the second difference + the first multiple" and the compensation time is back-calculated by "1–η_exp". This invention reduces the charging time error to <4%, while improving lifespan, reducing cost and thermal risk. The algorithm is lightweight and real-time and can be directly deployed in mass-produced BMS.

[0119] This invention predicts battery efficiency by combining future travel plans, and can extend cycle life by lowering the charging cutoff voltage in advance or adopting a segmented charging strategy. This significantly reduces the frequency and cost of battery replacement for users. Relying on universal historical parameters, it does not require hardware modifications for ternary lithium, lithium iron phosphate, or solid-state batteries. It can be adapted through software calibration, reducing the complexity and cost of platform development for OEMs. It couples user habits with the physical boundaries of the battery to form a self-evolving closed-loop management system, which improves user experience and takes into account the full life cycle value of battery assets. It is a smart energy management solution that balances economy and reliability.

[0120] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium is implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions can also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means implemented in a flow... Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should all be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent locomotive data acquisition and control management, characterized in that, Includes the following steps: Step S100: Allocate battery power based on historical usage habits and future travel plans, and formulate the first charging plan based on the allocation results; Step S200: Set the charging time according to the first charging plan, obtain historical battery parameters, predict battery efficiency based on historical battery parameters and future travel plans, and compensate for the charging time. Methods for allocating battery power based on historical usage habits and future travel plans include: Based on the average power consumption and the distance traveled per trip, the electricity consumption is calculated to obtain the seasonal characteristics of power consumption. Based on the city of travel and the city of future travel, the distribution of charging stations on highways is obtained and recorded as the first distribution. Based on the future travel cities, the distribution of charging piles within the cities is obtained and recorded as the second distribution situation; Based on the city of travel, the city of future travel, the time of future travel, and the seasonal characteristics of power consumption, a first assessment of vehicle power consumption on highways is conducted, and the first assessment results are obtained. Based on the future travel cities, future travel times, and seasonal characteristics of power consumption, a second assessment of vehicle power consumption within the city is conducted, resulting in the second assessment results. The second assessment result is represented as the location of target charging stations within the city; The distribution locations of various attractions in the future travel city are obtained. According to the path minimum planning method, a travel route map is obtained. The average traffic jam time of each road segment corresponding to the travel route map is obtained. The second speed is set as the average speed of urban travel. The third distance of each road segment is obtained. The distance that needs to be charged in the city is set as an unknown. The second ratio of the distance that needs to be charged in the city to the second speed is calculated. The first sum of the second ratio and the corresponding average traffic jam time is calculated. Setting the first sum to the corresponding duration in the power formula and the second ratio to the corresponding duration in the time formula, we obtain a linear equation in two variables representing the distance within the city that requires charging. This linear equation is expressed as follows: ； Where 's' represents the distance within the city where charging is required. , where v is the traffic jam duration, D is the first difference between the first and second battery levels, and p is the seasonality characteristic. Invalid solutions are deleted to obtain valid solutions; The effective solution is compared with the third distance. When the effective solution is greater than or equal to the third distance, the road segment is deleted and the nearest charging pile in the next road segment is selected and set as the target charging pile location in the city. When the effective solution is less than the third distance, the location of the charging pile corresponding to the road segment with the smallest difference between the road segment and the third distance and greater than the third distance is selected and set as the target charging pile location in the city. Repeat the process of comparing the valid solution with the third distance to obtain the location of the target charging pile within each city.

2. The intelligent locomotive data acquisition and control management method as described in claim 1, characterized in that: The historical usage habits include the cities visited, average power consumption, and distance traveled per trip. The future travel plan includes future travel cities, future travel routes, and future travel times; The allocation results include the first evaluation result and the second evaluation result; The first charging plan includes the locations of target charging stations on highways and the locations of target charging stations within cities; The historical battery parameters are represented as historical energy conversion efficiency.

3. The intelligent locomotive data acquisition and control management method as described in claim 2, characterized in that: The methods for formulating the first charging plan based on the allocation results include: Based on the first assessment results and the first distribution, the locations of target charging stations along the highway were determined. Based on the second assessment results and the second distribution, the locations of target charging stations within the city are obtained.

4. The intelligent locomotive data acquisition and control management method as described in claim 2, characterized in that: Based on historical battery parameters and future travel times, battery efficiency is predicted to obtain expected battery parameters. The charging time is compensated based on the expected battery parameters, and the compensated charging time is set as the final charging time.

5. The intelligent locomotive data acquisition and control management method as described in claim 1, characterized in that: Methods for allocating battery power based on historical usage habits and future travel plans include: Based on the average power consumption and the distance traveled per trip, the electricity consumption is calculated to obtain the seasonal characteristics of power consumption. The calculation method for the seasonal characteristics of the power consumption includes: The average power consumption and single-trip distance are obtained. According to the seasonal division standard, the average power consumption and single-trip distance are divided to obtain seasonal data. The seasonal data includes spring data, summer data, autumn data and winter data. Any set of data from any seasonal data is selected to calculate the first ratio of average power consumption to single-trip distance. Iterate through each of the first ratios for the seasons, calculate the first average of the first ratios, set the first average of the first ratios as the seasonal characteristic of the season, and iterate through the data for each season to obtain the seasonal characteristics of each season.

6. The intelligent locomotive data acquisition and control management method as described in claim 1, characterized in that: The first and second distribution scenarios represent the locations of charging stations on highways and within cities, respectively, obtained through a GPS system. The locations include longitude and latitude. Based on the city of travel, the city of future travel, the time of future travel, and the seasonal characteristics of power consumption, a first assessment of vehicle power consumption on highways is conducted, and the first assessment results are obtained. The first evaluation result represents the location of target charging stations on highways, and the second evaluation result represents the location of charging stations within cities. The method for the first evaluation includes: Obtain the driving city and the future travel city, obtain the highway route between the driving city and the future travel city through GPS, calculate the first distance of each highway route, sort the first distances in ascending order, select the first distance with the smallest value, and set the highway corresponding to the first distance with the smallest value as the target highway. The location of each charging station corresponding to the target highway is obtained through GPS. Based on the future travel time and seasonal classification criteria, the travel season is obtained, and the seasonal characteristics of power consumption corresponding to the travel season are matched. The power consumption when entering the highway is set as the first power consumption, and the second power consumption is set as the power consumption when charging is required. The first difference between the first power consumption and the second power consumption is calculated, the speed limit of the target highway is obtained, and the speed limit is set as the driving speed. The distance to be charged is calculated according to the time formula and the power formula. The formula for calculating the distance requiring charging is: ； ； ； Where S is the distance that needs to be charged, D is the first difference between the first and second battery levels, V is the driving speed, P is the total power of the distance that needs to be charged, and p is the seasonality characteristic. Obtain the second distance between the location of each charging pile corresponding to the target highway and the highway entrance, select the second distance with the smallest difference from the distance to be charged, and the second distance is less than or equal to the distance to be charged, and set the charging pile location as the target charging pile location of the highway. By iteratively selecting the second distance with the smallest difference from the distance to be charged, and the second distance being less than or equal to the charging pile corresponding to the distance to be charged, the locations of various target charging piles on the highway are obtained.

7. The intelligent locomotive data acquisition and control management method as described in claim 4, characterized in that: The method for predicting battery efficiency and obtaining expected battery parameters is as follows: Calculate the second difference of each adjacent historical energy conversion efficiency, calculate the average of the second differences, obtain the current time point, calculate the current time point and the future travel time point, calculate the third difference between the future travel time point and the current time point, calculate the ratio of the third difference to the recording period of the historical energy conversion efficiency, round the ratio of the third difference to the recording period of the historical energy conversion efficiency using the rounding method to obtain the first multiple, calculate the first product of the first multiple and the average of the second differences, calculate the difference between the most recently recorded historical energy conversion efficiency and the first product, and record it as the expected battery parameter; Methods for compensating for charging time based on expected battery parameters include: Obtain the current charging time, which is the charging time corresponding to the first difference obtained based on experience. Calculate the difference between 1 and the expected battery parameters, and record it as the first ratio. Calculate the quotient time of the current charging time and the first ratio, and update the quotient time as the new charging time.

8. A smart locomotive data acquisition and control management system, the system being used to execute the smart locomotive data acquisition and control management method according to claim 1, characterized in that, Includes a planning module and a compensation module; The planning module allocates power according to historical usage habits and future travel plans, and formulates a first charging plan based on the allocation results; The compensation module sets the charging time according to the first charging plan, obtains historical battery parameters, predicts battery efficiency based on historical battery parameters and future travel plans, and compensates for the charging time.

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