A data-driven intelligent device resource scheduling and energy efficiency management method

Abstract

The present application relates to the technical field of closed factory area, port automatic driving tractor operation, and particularly relates to a data-driven intelligent equipment resource scheduling and energy efficiency management method, comprising the following steps: S1: connecting the automatic driving platform and the field operation management system; S2: based on the standardized data; S3: constructing a double-mode intelligent scheduling system of on-demand vehicle and reservation vehicle; S4: constructing an integrated vehicle scheduling large screen; S5: based on double-source data collection vehicle energy efficiency index; S6: completing the standardized and tamper-proof archiving of the whole-process operation data. The present application unifies the vehicle body data of the automatic driving platform and the business data of the field operation management system through unified collection and standardized processing, solves the data island problem that the two types of data are independent and cannot be linked in the traditional scheme, realizes the real-time fusion of the vehicle body state and the field working condition, and provides a complete and reliable data basis for scheduling decision, vehicle control instruction issuing and energy efficiency analysis.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving tractor operation technology in closed factory areas and ports, specifically to a data-driven intelligent equipment resource scheduling and energy efficiency management method. Background Technology

[0002] This technology is applied to the large-scale operation of autonomous driving tractors in closed factory areas, ports, and other similar scenarios. Currently, the industry has many technical shortcomings in equipment management, vehicle scheduling, on-site visualization, and energy efficiency management, making it difficult to meet the requirements of industrialized and standardized operations. Specific problems are as follows: The problem of data silos is significant: the vehicle body data of autonomous driving tractors belongs to different systems from on-site dispatch, work orders, station and other operational data. They have not achieved standardized integration and linkage, resulting in a lack of complete data support for dispatch decisions and vehicle control commands. This can easily lead to mismatches between operations and on-site working conditions and vehicle status. Insufficient precision vehicle control capabilities: The existing system only supports basic vehicle start-stop operations, lacks the 1-3 meter level precise micro-movement vehicle relocation function required for loading and unloading scenarios in the factory area, and has not established a hierarchical vehicle control system for locking, unlocking, starting and stopping vehicles, resulting in poor vehicle control security and scenario adaptability. Weak visualization of operation and management: The existing solution can only display the basic location of vehicles. It has not built a comprehensive operation screen that integrates monitoring, dispatching, vehicle control, alarms and traceability. It cannot simultaneously present full-dimensional information such as video, vehicle status, trip, and faults. Operation personnel have difficulty grasping the overall situation and the efficiency of emergency response is low. Resource scheduling and energy efficiency management are disconnected: various resources such as tractors, trailers, and onboard equipment cannot be scheduled in an integrated manner, resulting in low resource utilization; vehicle energy consumption data and operational data are independent of each other, making it impossible to optimize scheduling strategies based on data and achieve refined energy consumption control, resulting in high operating costs; Lack of a full-process traceability system: Data from the entire process, including scheduling tasks, vehicle control commands, and fault handling, is not archived in a standardized manner, making it difficult to trace back historical records. This not only makes it impossible to accurately assign responsibility for operational accidents but also fails to provide data support for optimizing operational processes. Summary of the Invention

[0003] The purpose of this invention is to provide a data-driven intelligent device resource scheduling and energy efficiency management method to solve the above-mentioned problems, thereby resolving the issues mentioned in the background art.

[0004] To address the aforementioned issues, this invention provides a technical solution: a data-driven intelligent device resource scheduling and energy efficiency management method, comprising the following steps: S1: Connecting the Yushi autonomous driving platform and the on-site operation management system, collecting full vehicle data and full-dimensional on-site operation data in real time, and outputting unified standardized data after standardized cleaning and low-latency caching;

[0005] S2: Based on the standardized data, construct a full lifecycle management and control system for vehicle-mounted intelligent equipment, tractor and trailer resources to achieve optimal resource allocation and safety management;

[0006] S3: Construct a dual-mode intelligent dispatch system for immediate and scheduled car use to achieve full lifecycle management of dispatch tasks; based on dual-source data linkage verification, construct a hierarchical vehicle control system covering start-up, shutdown, locking, unlocking, and fine-grained micro-movements before and after, to achieve full-link control of vehicle control commands;

[0007] S4: Construct an integrated vehicle dispatching dashboard to achieve real-time feedback of vehicle monitoring videos, core parameters, planned routes, map location trajectories, dispatching operations, task status and fault alarms in all dimensions, and realize the data linkage of all modules and the integrated closed loop of monitoring-dispatch-vehicle control;

[0008] S5: Based on dual-source data acquisition of vehicle energy efficiency indicators, a data-driven energy efficiency optimization scheduling strategy is constructed and an energy efficiency analysis report is generated, where the vehicle energy efficiency index E is defined as:

[0009] ;

[0010] in, Let be the mileage traveled by the i-th vehicle. For its energy consumption, This is a correction factor for scheduling efficiency.

[0011] S6: Complete the standardization and tamper-proof archiving of data throughout the entire operation process, build a multi-dimensional traceability and query system, and realize full traceability of the operation process.

[0012] Preferably, the data collection fields of the Yushi platform vehicle body data in S1 include: unique vehicle number, trailer number, real-time latitude and longitude, heading angle, driving speed, remaining battery power, cumulative mileage, four tire pressure and tire temperature, driving status, lock status, autonomous driving mode, fault code, obstacle alarm information and real-time video stream of vehicle monitoring.

[0013] The on-site operation data includes: project information, operation routes, work site coordinates, planned itinerary, departure area, dispatch task records, operator permissions and loading and unloading status. The standardized cleaning rules include protocol parsing, deduplication, invalid value filtering and format unification. The low-latency caching mechanism uses RabbitMQ and Redis, with a refresh latency of ≤500ms.

[0014] Preferably, the refined micro-motion vehicle control method described in S3 includes: supporting bidirectional selection between front and rear and vehicle control commands at three fixed distances of 1 meter, 2 meters, and 3 meters; performing dual-source data linkage pre-verification before issuing the vehicle control command; generating a unique number after passing the verification; recording the commander, time, content, verification result, and execution status; classifying the scheduling task status into queue waiting, in progress, historical end, and historical scheduling; and providing an event list to achieve full lifecycle tracking.

[0015] Preferably, the integrated vehicle dispatching screen in S4 includes eight functional modules: global control navigation, real-time vehicle monitoring and parameter display, visualization of planned trip progress, map visualization, integrated dispatching operation, full-state management of dispatching tasks, visualization of hierarchical fault alarms, and a full-module linkage mechanism. The full-module linkage mechanism supports automatic and synchronous switching of monitoring videos, parameters, trips, and dispatching panels after selecting a vehicle on the map, realizing a closed-loop operation without switching.

[0016] Preferably, the energy efficiency optimization scheduling strategy in S5 includes: prioritizing the matching of vehicles with the best energy consumption and the closest location during scheduling, and automatically planning the energy-optimized driving route based on energy consumption characteristics and route conditions. The energy efficiency indicators include energy consumption per unit mileage, empty-run rate, vehicle utilization rate, and ineffective stop duration, wherein the empty-run rate R is calculated using the following formula:

[0017] ;

[0018] in, This refers to the empty mileage. This represents the total mileage traveled.

[0019] Preferably, the integrated matching and scheduling method for tractor and trailer described in S2 includes: constructing a dynamic schedulable resource pool, updating the association, availability, location, power, and health of tractor and trailer in real time, automatically verifying matching compliance to avoid overloading or mismatch operations, and the vehicle locking and unlocking hierarchical safety control mechanism supporting one-click locking of faulty or non-compliant vehicles, prohibiting driving commands in the locked state, and only authorized personnel can unlock and restore scheduling permissions.

[0020] A data-driven intelligent device resource scheduling and energy efficiency management system includes: a dual-source data acquisition module for connecting to the Yushi autonomous driving platform and the on-site operation management system to collect vehicle and operation data in real time;

[0021] The data standardization processing module is used to clean, standardize, and cache multi-source data with low latency.

[0022] The intelligent equipment resource management module is used to realize the full life cycle management and integrated scheduling of vehicle-mounted equipment, tractor and trailer;

[0023] The intelligent scheduling and refined vehicle control module is used to realize the management of basic scheduling resources, dual-mode scheduling, hierarchical vehicle control command verification and issuance, and full life cycle management of scheduling tasks;

[0024] The integrated visualization large screen module is used to realize real-time feedback of operational data across all dimensions and to achieve a closed-loop integrated monitoring, dispatching, and vehicle control system.

[0025] The energy efficiency management and optimization module is used to collect and statistically analyze energy efficiency data, optimize scheduling strategies, and generate energy efficiency analysis reports.

[0026] The data archiving and traceability module is used to realize standardized archiving, multi-dimensional traceability query, and operational statistical analysis of full-process operational data.

[0027] The modules described above communicate with each other sequentially and work together to implement all the steps of the method described above.

[0028] Preferably, the integrated visualization large screen module has a built-in integrated dispatch operation panel and quick vehicle control entry, and realizes real-time visualization of operating routes, stations, vehicle locations and trajectories based on the Gaode Map engine. The matching degree M between the real-time vehicle location and the planned trip progress is calculated as follows:

[0029] ;

[0030] in, This is the actual arrival time. For the planned arrival time, To prevent the removal of the zero constant.

[0031] Preferably, the energy efficiency management and optimization module automatically generates vehicle energy efficiency analysis reports on a daily, weekly, and monthly basis, statistically analyzing overall project energy consumption, individual vehicle energy consumption rankings, energy consumption anomaly warnings, and energy-saving optimization suggestions. In the scheduling strategy optimization, the vehicle comprehensive scheduling priority P is determined by the following formula:

[0032] ;

[0033] in, For vehicle energy efficiency index, This represents the distance between the vehicle and the starting point of the mission. For equipment health, These are the weighting coefficients.

[0034] Preferably, the data archiving and traceability module supports multi-dimensional combined queries by operation project, vehicle number, time range, vehicle control command type, event type, and operator, realizing full-process traceability of historical dispatch record playback, vehicle control command traceability, and fault event handling. The archived data adopts a dual-write mechanism of relational database and time-series database and is set to be tamper-proof storage.

[0035] The beneficial effects of this invention are reflected in:

[0036] 1. This invention solves the problem of data silos in traditional solutions by unifying and standardizing the collection of vehicle data from the Yushi autonomous driving platform and the business data of the on-site operation management system. It realizes the real-time fusion of vehicle status and on-site working conditions, providing a complete and reliable data foundation for scheduling decisions, vehicle control command issuance and energy efficiency analysis.

[0037] 2. This invention constructs a hierarchical and refined vehicle control system covering start-up, shutdown, locking and unlocking, and multi-position micro-motion vehicle control in the forward and backward directions. Combined with a front-end safety verification mechanism based on dual-source data linkage, it solves the problem that the existing dispatching system has limited vehicle control capabilities and cannot adapt to loading and unloading positioning scenarios. It achieves full-link traceability and control of vehicle control commands and improves the safety of vehicle control operations.

[0038] 3. This invention constructs an integrated visual screen that combines monitoring, dispatching, vehicle control, alarms, and traceability. It enables real-time display and module linkage of vehicle video, core parameters, planned routes, map trajectories, dispatching tasks, and fault alarms across all dimensions. This solves the problem that on-site operators cannot have a comprehensive grasp of the real-time status of vehicles. It achieves seamless closed-loop operation of vehicle selection, status monitoring, vehicle control, and dispatching, thereby improving the overall efficiency of operation management and emergency response. Attached Figure Description

[0039] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0040] Figure 1 This is a schematic diagram of the dual-source data acquisition and fusion process of the present invention;

[0041] Figure 2 This is a schematic diagram of the intelligent scheduling and refined vehicle control execution process of the present invention;

[0042] Figure 3 This is a schematic diagram of the visual large screen linkage and energy efficiency management closed loop of the present invention. Detailed Implementation

[0043] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0044] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0045] Example 1

[0046] like Figures 1-3 As shown, a data-driven intelligent device resource scheduling and energy efficiency management method includes the following steps: S1: Connecting the Yushi autonomous driving platform and the on-site operation management system, collecting full vehicle data and full-dimensional on-site operation data in real time, and outputting unified standardized data after standardized cleaning and low-latency caching;

[0047] S2: Based on standardized data, construct a full lifecycle management and control system for vehicle-mounted intelligent equipment, tractor units, and trailer resources to achieve optimal resource allocation and safety management;

[0048] S3: Construct a dual-mode intelligent dispatch system for immediate and scheduled car use to achieve full lifecycle management of dispatch tasks; based on dual-source data linkage verification, construct a hierarchical vehicle control system covering start-up, shutdown, locking, unlocking, and fine-grained micro-movements before and after, to achieve full-link control of vehicle control commands;

[0049] S4: Construct an integrated vehicle dispatching dashboard to achieve real-time feedback of vehicle monitoring videos, core parameters, planned routes, map location trajectories, dispatching operations, task status and fault alarms in all dimensions, and realize the data linkage of all modules and the integrated closed loop of monitoring-dispatch-vehicle control;

[0050] S5: Based on dual-source data acquisition of vehicle energy efficiency indicators, a data-driven energy efficiency optimization scheduling strategy is constructed and an energy efficiency analysis report is generated, where the vehicle energy efficiency index E is defined as:

[0051] ;

[0052] in, Let be the mileage traveled by the i-th vehicle. For its energy consumption, This is a correction factor for scheduling efficiency.

[0053] S6: Complete the standardization and tamper-proof archiving of data throughout the entire operation process, build a multi-dimensional traceability and query system, and realize full traceability of the operation process.

[0054] Preferably, the vehicle body data collection fields of the Yushi platform in S1 include: unique vehicle number, trailer number, real-time latitude and longitude, heading angle, driving speed, remaining battery power, cumulative mileage, tire pressure and temperature of all four tires, driving status, lock status, autonomous driving mode, fault codes, obstacle alarm information and real-time video stream of vehicle monitoring.

[0055] On-site operational data includes: project information, operating routes, work site coordinates, planned routes, departure areas, dispatch task records, operator permissions and loading / unloading status. Standardized cleaning rules include protocol parsing, deduplication, invalid value filtering and format unification. The low-latency caching mechanism uses RabbitMQ and Redis, with a refresh latency of ≤500ms.

[0056] Furthermore, the refined micro-motion vehicle control method in S3 includes: supporting two-way selection between front and rear and vehicle control commands at three fixed distances of 1 meter, 2 meters, and 3 meters; performing dual-source data linkage pre-verification before issuing vehicle control commands; generating a unique number after passing the verification; recording the commander, time, content, verification result, and execution status; and classifying the scheduling task status into queue waiting, in progress, historical end, and historical scheduling, with a corresponding event list to achieve full lifecycle tracking.

[0057] Furthermore, the integrated vehicle dispatch screen in S4 includes eight functional modules: global control navigation, real-time vehicle monitoring and parameter display, visualization of planned trip progress, map visualization, integrated dispatch operation, full status management of dispatch tasks, hierarchical fault alarm visualization, and a full module linkage mechanism. The full module linkage mechanism supports automatic and synchronous switching of monitoring videos, parameters, trips, and dispatch panels after selecting a vehicle on the map, achieving seamless closed-loop operation.

[0058] Furthermore, the energy efficiency optimization scheduling strategy in S5 includes: prioritizing the matching of vehicles with the best energy consumption and the closest location during scheduling, and automatically planning the energy-optimized driving route based on energy consumption characteristics and route conditions. Energy efficiency indicators include energy consumption per unit mileage, empty-run rate, vehicle utilization rate, and ineffective stop duration. The empty-run rate R is calculated using the following formula:

[0059] ;

[0060] in, This refers to the empty mileage. This represents the total mileage traveled.

[0061] Furthermore, the integrated matching and scheduling method for tractor units and trailers in S2 includes: constructing a dynamic and schedulable resource pool, updating the association, availability, location, power, and health of tractor units and trailers in real time, automatically verifying matching compliance to avoid overloading or mismatch operations, and a graded safety control mechanism for vehicle locking and unlocking that supports one-click locking of faulty or non-compliant vehicles, prohibiting driving commands while locked, and allowing only authorized personnel to unlock and restore scheduling permissions.

[0062] A data-driven intelligent device resource scheduling and energy efficiency management system includes: a dual-source data acquisition module for connecting to the Yushi autonomous driving platform and the on-site operation management system to collect vehicle and operation data in real time;

[0063] The data standardization processing module is used to clean, standardize, and cache multi-source data with low latency.

[0064] The intelligent equipment resource management module is used to realize the full life cycle management and integrated scheduling of vehicle-mounted equipment, tractor and trailer;

[0065] The intelligent scheduling and refined vehicle control module is used to realize the management of basic scheduling resources, dual-mode scheduling, hierarchical vehicle control command verification and issuance, and full life cycle management of scheduling tasks;

[0066] The integrated visualization large screen module is used to realize real-time feedback of operational data across all dimensions and to achieve a closed-loop integrated monitoring, dispatching, and vehicle control system.

[0067] The energy efficiency management and optimization module is used to collect and statistically analyze energy efficiency data, optimize scheduling strategies, and generate energy efficiency analysis reports.

[0068] The data archiving and traceability module is used to realize standardized archiving, multi-dimensional traceability query, and operational statistical analysis of full-process operational data.

[0069] The modules mentioned above communicate with each other sequentially and work together to implement all the steps of the above method.

[0070] Furthermore, the integrated visual large screen module incorporates an integrated dispatch operation panel and a quick vehicle control entry, and uses the Gaode Map engine to achieve real-time visualization of operating routes, stations, vehicle locations, and trajectories. The matching degree M between the real-time vehicle location and the planned trip progress is calculated as follows:

[0071] ;

[0072] in, This is the actual arrival time. For the planned arrival time, To prevent the removal of the zero constant.

[0073] Furthermore, the energy efficiency management and optimization module automatically generates vehicle energy efficiency analysis reports on a daily, weekly, and monthly basis, statistically analyzing overall project energy consumption, individual vehicle energy consumption rankings, energy consumption anomaly warnings, and energy-saving optimization suggestions. In the scheduling strategy optimization, the vehicle comprehensive scheduling priority P is determined by the following formula:

[0074] ;

[0075] in, For vehicle energy efficiency index, This represents the distance between the vehicle and the starting point of the mission. For equipment health, These are the weighting coefficients.

[0076] Furthermore, the data archiving and traceability module supports multi-dimensional combined queries by operation project, vehicle number, time range, vehicle control command type, event type, and operator, realizing the full-process traceability of historical dispatch record playback, vehicle control command traceability, and fault event handling. The archived data adopts a dual-write mechanism of relational database and time-series database and is set to be tamper-proof storage.

[0077] Example 2

[0078] This embodiment specifically illustrates the typical implementation of steps S1 to S5, especially the calculation process of the energy efficiency index E;

[0079] In an autonomous driving tractor operation project at a paper mill, the system collects real-time vehicle data at a frequency of ≥1Hz via the UISEE platform SDK, and simultaneously collects scheduling task data via the MQTT interface of the on-site operation system. Both types of data are cached in Redis and then uniformly input into the energy efficiency management module.

[0080] The following is the operational data of three autonomous driving tractors during the time period of 08:00-20:00 on a certain weekday:

[0081]

[0082] The system uses the following energy efficiency index calculation formula:

[0083]

[0084] Substitute the data:

[0085]

[0086]

[0087]

[0088] Pick =0.98, the overall scheduling efficiency correction factor for the day:

[0089]

[0090] Should ≈6.20 is used as the comprehensive energy efficiency index of the fleet on that day. The system uses this value to judge the effectiveness of the dispatching strategy. When E is lower than the historical average, an energy efficiency optimization alarm is automatically triggered.

[0091] Example 3

[0092] This embodiment specifically illustrates the refined micro-motion vehicle control method, especially the generation of front and rear bidirectional, three-level distance vehicle control commands and the dual-source data linkage pre-verification logic;

[0093] In a loading and unloading alignment scenario within the factory area, the tractor T005 needs to move backward 2 meters to align with the loading port. The operator selects the appropriate option on the integrated large-screen dispatch panel:

[0094] Vehicle: T005;

[0095] Direction: Back;

[0096] Distance: 2 meters;

[0097] Click "Execute Now";

[0098] The system triggers a pre-verification rule, and the verification function V is defined as follows:

[0099]

[0100] in, For vehicle status verification, in case of lockout or malfunction. =0, the normal value is 1;

[0101] For detecting obstacles behind, the distance to the obstacle is less than 2.5 meters. =0, otherwise 1;

[0102] For geofence verification, when exceeding the allowable micro-movement area =0, otherwise 1;

[0103] In this verification:

[0104] Vehicle T005 locked state = unlocked. =1;

[0105] The nearest obstacle behind is 3.8 meters away. =1;

[0106] The current position is within the permissible micro-movement area. =1;

[0107]

[0108] If the verification passes, the system generates a unique vehicle control command number, for example: CTL-20241022-005-002;

[0109] Record the person giving the instruction, the time, and the content; issue the instruction to the Yushi platform; the vehicle accurately reverses 2 meters; and the execution status is transmitted back and recorded in real time.

[0110] If the verification fails, for example, V=0, the system will refuse to issue the command and display an alarm on the large screen, indicating the specific reason for the failure.

[0111] Example 4

[0112] This embodiment specifically illustrates the energy efficiency optimization scheduling strategy, especially the scheduling optimization process based on the empty running rate R;

[0113] In a port autonomous driving tractor project, the system provides real-time statistics on the daily empty mileage and total mileage of four vehicles as follows:

[0114]

[0115] According to the empty-running rate calculation formula:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] The system sets the empty running rate threshold. =15%, P002 and P004 exceed the threshold, triggering scheduling optimization;

[0122] The system invokes the energy efficiency optimization scheduling strategy. When the next round of scheduling tasks is issued, it prioritizes matching the vehicle whose current location is closest to the task start point; it automatically plans the route based on energy consumption characteristics and line conditions; for vehicles with high empty running rates, it temporarily reduces their scheduling priority and prioritizes scheduling vehicles with low empty running rates such as P003.

[0123] The system also generated optimization suggestions: "P004 vehicle's empty running rate has reached 20%. It is recommended to check whether the route planning is detoured or whether the loading and unloading points are reasonably matched."

[0124] Example 5

[0125] This embodiment specifically illustrates the method for calculating the matching degree M and its application in the large-screen linkage mechanism;

[0126] In a paper mill tractor project, the system calculates the planning route matching degree for each dispatched vehicle in real time, which is used for large-screen visualization and abnormal early warning.

[0127] Suppose that in a certain planned journey, the planned arrival time of vehicle T008 at station S3 is... =10:30:00, actual arrival time is =10:32:15;

[0128] Take the zero constant = 1 second, which is approximately 0.000278 hours (1 / 3600 ≈ 0.000278 hours).

[0129]

[0130]

[0131]

[0132] Matching degree calculation formula:

[0133]

[0134] Substitute:

[0135]

[0136] That is, M≈99.64%, indicating that the vehicle's trip matching degree is extremely high, and the task progress bar on the large screen is displayed as a green highlight;

[0137] If another vehicle, T009, is severely delayed:

[0138]

[0139]

[0140]

[0141] When M < 0.98, the system automatically pushes "Vehicle T009 trip progress abnormal, delay exceeds 20 minutes" to the fault alarm module on the large screen, and supports one-click tracing of the cause of the delay;

[0142] This matching degree index, together with the energy efficiency index E and the empty running rate R, forms the core quantitative evaluation system for system energy efficiency and scheduling quality.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A data-driven intelligent device resource scheduling and energy efficiency management method, characterized in that: Includes the following steps: S1: Connects to the UISEE autonomous driving platform and the on-site operation management system, collects full vehicle data and full-dimensional on-site operation data in real time, and outputs unified standardized data after standardized cleaning and low-latency caching; S2: Based on the standardized data, construct a full lifecycle management and control system for vehicle-mounted intelligent equipment, tractor and trailer resources to achieve optimal resource allocation and safety management; S3: Construct a dual-mode intelligent dispatch system for immediate and scheduled car use to achieve full lifecycle management of dispatch tasks; based on dual-source data linkage verification, construct a hierarchical vehicle control system covering start-up, shutdown, locking, unlocking, and fine-grained micro-movements before and after, to achieve full-link control of vehicle control commands; S4: Construct an integrated vehicle dispatching dashboard to achieve real-time feedback of vehicle monitoring videos, core parameters, planned routes, map location trajectories, dispatching operations, task status and fault alarms in all dimensions, and realize the data linkage of all modules and the integrated closed loop of monitoring-dispatch-vehicle control; S5: Based on dual-source data acquisition of vehicle energy efficiency indicators, a data-driven energy efficiency optimization scheduling strategy is constructed and an energy efficiency analysis report is generated, where the vehicle energy efficiency index E is defined as: ； in, Let be the mileage traveled by the i-th vehicle. For its energy consumption, This is a correction factor for scheduling efficiency. S6: Complete the standardization and tamper-proof archiving of data throughout the entire operation process, build a multi-dimensional traceability and query system, and realize full traceability of the operation process.

2. The data-driven intelligent device resource scheduling and energy efficiency management method according to claim 1, characterized in that: The data collection fields of the Yushi platform body described in S1 include: unique vehicle number, trailer number, real-time latitude and longitude, heading angle, driving speed, remaining battery power, cumulative mileage, four tire pressure and tire temperature, driving status, lock status, autonomous driving mode, fault code, obstacle alarm information and real-time video stream of vehicle monitoring. The on-site operation data includes: project information, operation routes, work site coordinates, planned itinerary, departure area, dispatch task records, operator permissions and loading and unloading status. The standardized cleaning rules include protocol parsing, deduplication, invalid value filtering and format unification. The low-latency caching mechanism uses RabbitMQ and Redis, with a refresh latency of ≤500ms.

3. The data-driven intelligent device resource scheduling and energy efficiency management method according to claim 1, characterized in that: The refined micro-motion vehicle control method described in S3 includes: supporting two-way selection between front and rear and vehicle control commands at three fixed distances of 1 meter, 2 meters, and 3 meters; performing dual-source data linkage pre-verification before issuing vehicle control commands; generating a unique number after passing the verification; recording the commander, time, content, verification result, and execution status; classifying the scheduling task status into queue waiting, in progress, historical end, and historical scheduling; and providing an event list to achieve full lifecycle tracking.

4. The data-driven intelligent device resource scheduling and energy efficiency management method according to claim 1, characterized in that: The integrated vehicle dispatching screen in S4 includes eight functional modules: global control and navigation, real-time vehicle monitoring and parameter display, visualization of planned trip progress, map visualization, integrated dispatching operation, full status management of dispatching tasks, hierarchical fault alarm visualization, and a full module linkage mechanism. The full module linkage mechanism supports automatic and synchronous switching of monitoring videos, parameters, trips, and dispatching panels after selecting a vehicle on the map, realizing seamless closed-loop operation.

5. The data-driven intelligent device resource scheduling and energy efficiency management method according to claim 1, characterized in that: The energy efficiency optimization scheduling strategy described in S5 includes: prioritizing the matching of vehicles with the best energy consumption and the closest location during scheduling, and automatically planning the optimal energy consumption driving route based on energy consumption characteristics and route conditions. The energy efficiency indicators include energy consumption per unit mileage, empty-run rate, vehicle utilization rate, and ineffective stop duration, wherein the empty-run rate R is calculated using the following formula: ;in, This refers to the empty mileage. This represents the total mileage traveled.

6. The data-driven intelligent device resource scheduling and energy efficiency management method according to claim 1, characterized in that: The integrated matching and scheduling method for tractor and trailer described in S2 includes: constructing a dynamic schedulable resource pool, updating the association, availability, location, power, and health of tractor and trailer in real time, automatically verifying matching compliance to avoid overloading or mismatch operations, and the vehicle locking and unlocking hierarchical safety control mechanism supporting one-click locking of faulty or illegal vehicles, prohibiting driving commands in the locked state, and only authorized personnel can unlock and restore scheduling permissions.

7. A data-driven intelligent device resource scheduling and energy efficiency management system, characterized in that, include: The dual-source data acquisition module is used to connect to the UISEE autonomous driving platform and the on-site operation management system to collect vehicle and operation data in real time. The data standardization processing module is used to clean, standardize, and cache multi-source data with low latency. The intelligent equipment resource management module is used to realize the full life cycle management and integrated scheduling of vehicle-mounted equipment, tractor and trailer; The intelligent scheduling and refined vehicle control module is used to realize the management of basic scheduling resources, dual-mode scheduling, hierarchical vehicle control command verification and issuance, and full life cycle management of scheduling tasks; The integrated visualization large screen module is used to realize real-time feedback of operational data across all dimensions and to achieve a closed-loop integrated monitoring, dispatching, and vehicle control system. The energy efficiency management and optimization module is used to collect and statistically analyze energy efficiency data, optimize scheduling strategies, and generate energy efficiency analysis reports. The data archiving and traceability module is used to realize standardized archiving, multi-dimensional traceability query, and operational statistical analysis of full-process operational data. The modules described above are connected in sequence to collaboratively implement all the steps of the method described in any one of claims 1 to 6.

8. The data-driven intelligent device resource scheduling and energy efficiency management system according to claim 7, characterized in that: The integrated visualization large screen module has a built-in integrated dispatch operation panel and quick vehicle control entry, and uses the Gaode Map engine to realize real-time visualization of operating routes, stations, vehicle locations and trajectories. The matching degree M between the real-time vehicle location and the planned trip progress is calculated as follows: ； in, This is the actual arrival time. For the planned arrival time, To prevent the removal of the zero constant.

9. The data-driven intelligent device resource scheduling and energy efficiency management system according to claim 7, characterized in that: The energy efficiency management and optimization module automatically generates vehicle energy efficiency analysis reports on a daily, weekly, and monthly basis, statistically analyzing overall project energy consumption, individual vehicle energy consumption rankings, energy consumption anomaly warnings, and energy-saving optimization suggestions. In the scheduling strategy optimization, the vehicle comprehensive scheduling priority P is determined by the following formula: ； in, For vehicle energy efficiency index, This represents the distance between the vehicle and the starting point of the mission. For equipment health, These are the weighting coefficients.

10. A data-driven intelligent device resource scheduling and energy efficiency management system according to claim 7, characterized in that: The data archiving and traceability module supports multi-dimensional queries based on operational projects, vehicle numbers, time ranges, vehicle control command types, event types, and operators. It enables the playback of historical dispatch records, the tracing of vehicle control commands, and the full-process tracing of fault event handling. The archived data adopts a dual-write mechanism of relational database and time-series database and is set to be stored in an immutable manner.

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