Maneuvering energy supply device and system based on unified data base

Abstract

The invention relates to the technical field of mobile energy supply network intelligent control based on a unified data base, in particular to a mobile energy supply device and system based on the unified data base, and a data acquisition module obtains vehicle running state parameters, energy demand signals and supply facility state information and outputs acquisition signals. The data integration module performs protocol conversion and format alignment on the acquired signals to generate a structured data stream. The demand analysis module processes the structured data stream based on an algorithm model and outputs replenishment instruction parameters. And the control instruction module encodes the supply instruction parameter into a control command which can be identified by the oiling machine. And the execution module drives the oiling machine to act according to the control command, generates state data including the actual oiling amount and feeds back the state data to the data acquisition module to form a closed-loop data flow. The system realizes automatic circulation and control of data through modular design, and solves the technical problems of low energy supply efficiency, high error rate and difficult safety management and control.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for mobile energy supply networks based on a unified data platform, and particularly to mobile energy supply devices and systems based on a unified data platform. Background Technology

[0002] A unified data foundation is a centralized data architecture that uses standardized protocols to integrate heterogeneous data from multiple sources, such as vehicle operating status, energy demand, and replenishment facilities, to build a consistent and interoperable data foundation that supports information sharing and collaboration at the network level. Mobile energy replenishment refers to the use of mobile replenishment units, such as mobile charging vehicles, to dynamically adjust deployment locations and resource allocation based on real-time data to adapt to fluctuations in fleet energy demand and improve the response efficiency and coverage of the replenishment network.

[0003] Existing mobile energy replenishment technologies suffer from the following technical pain points: Specifically, in intelligent fleet energy replenishment network management applications, the system heavily relies on manual operation for refueling processes. For example, employees must manually insert cards, input preset values, and complete identity verification, introducing operational delays and the risk of human error. Furthermore, data silos exist between subsystems such as refueling machine controllers, network management platforms, and data query modules, lacking real-time data synchronization and integration, resulting in information sharing difficulties. For instance, during fleet refueling, refueling personnel must repeatedly perform card authentication and parameter settings. Operational errors may lead to refueling deviations or safety incidents, and the management system cannot obtain refueling status data in real time, resulting in low replenishment efficiency, increased error rates, and reliance on decentralized manual inspections for safety monitoring, making unified control difficult. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a mobile energy replenishment device and system based on a unified data platform. This invention solves the technical problems of low energy replenishment efficiency, high error rate, and difficulty in safety management caused by reliance on manual operation and data silos in the system.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows:

[0006] In a first aspect, the mobile energy replenishment device based on a unified data platform provided by the present invention includes: The system comprises a data acquisition module, a data integration module, a requirements analysis module, a control command module, and an execution module. The data acquisition module obtains vehicle operating status parameters, energy demand signals, and supply facility status information, and outputs the acquired signals. The data integration module receives the acquisition signal output by the data acquisition module, performs protocol conversion and format alignment on the acquisition signal, and generates a structured data stream. The demand analysis module receives the structured data stream generated by the data integration module, processes the structured data stream based on the algorithm model, and outputs supply instruction parameters. The control command module receives the replenishment command parameters output by the demand analysis module and encodes the replenishment command parameters into control commands that the refueling machine can recognize. The execution module receives control commands encoded by the control command module, drives the fuel dispenser to operate according to the control commands, and generates status data including the actual amount of fuel dispensed, which is then fed back to the data acquisition module.

[0007] Furthermore, in the mobile energy replenishment device based on a unified data platform described in this invention, the data acquisition module is also used for: The analog voltage signal is obtained through the vehicle-mounted fuel tank level sensor, the digital signal of the vehicle position coordinates is obtained through the GPS locator, the liquid level current signal is obtained through the fuel tank inventory sensor, and the working status pulse signal is obtained through the fuel dispenser encoder. The analog voltage signal and the liquid level height current signal are filtered and converted from analog to digital to generate a standard electrical signal; The standard electrical signal, the vehicle position coordinate digital signal, and the working status pulse signal are packaged together and output as the acquisition signal through the RS-485 communication protocol.

[0008] Furthermore, in the mobile energy replenishment device based on a unified data platform described in this invention, the data integration module is also used for: Receive the acquisition signal, wherein the acquisition signal is a data packet using the RS-485 communication protocol; The RS-485 communication protocol data packets are parsed into TCP / IP data packets; Perform format verification on the data within the TCP / IP data packets and remove abnormal data points whose values ​​exceed a preset range; Convert the validated and rejected data into JSON format data objects; After adding timestamp metadata tags to the JSON format data object, it is written to a relational database, generating a structured data stream with timestamp indexes and outputting it.

[0009] Furthermore, in the mobile energy replenishment device based on a unified data platform described in this invention, the demand analysis module is also used for: Receive the structured data stream; The vehicle location coordinates, fuel tank inventory, and fuel dispenser status parameters are parsed from the structured data stream. The parsed vehicle location coordinates, fuel tank inventory, and fuel dispenser status parameters are input into the algorithm model; the algorithm model uses a greedy algorithm to calculate the shortest path and a linear programming method to optimize resource allocation and generate a supply task queue. Based on the supply task queue output by the algorithm model, supply instruction parameters including target refueling amount, vehicle execution priority, and safety threshold are generated.

[0010] Furthermore, in the mobile energy replenishment device based on a unified data platform described in this invention, the control command module is also used for: Receive the supply command parameters; Based on the refueling instruction parameters and the refueling machine communication protocol, a binary encoded operation sequence is generated; An authentication token is generated using the AES encryption algorithm, and the authentication token is embedded in the binary encoded operation sequence; The binary-coded operation sequence with embedded authentication tokens is encapsulated into a transmission data packet using a 4G communication module and the MQTT protocol. A cyclic redundancy check code is added to the transmitted data packet and sent to the execution module.

[0011] Furthermore, in the mobile energy replenishment device based on a unified data platform described in this invention, the execution module is also used for: Receive the control command, wherein the control command is a binary encoded operation sequence; The binary encoded operation sequence is analyzed to extract the preset refueling amount and the solenoid valve control command; According to the solenoid valve control command, the fuel dispenser controller is driven to trigger the nozzle lifting detection sensor and control the opening degree of the solenoid valve. During the refueling process, the real-time refueling volume is monitored, and when the real-time refueling volume reaches the preset refueling volume value, a shutdown command is generated to close the solenoid valve. After the solenoid valve is closed, the payment is deducted and settled through the IC card reader, and the actual amount of fuel, timestamp and settlement information are obtained. The actual refueling amount, timestamp, and settlement information are fed back to the data acquisition module as status data.

[0012] Furthermore, the mobile energy replenishment device based on a unified data platform described in this invention also includes: The unified data base receives a structured data stream with a timestamp index written by the data integration module; Build a distributed hash index for the structured data stream written to the relational database; Receive data query requests sent by the requirements analysis module; Based on the data query request, the distributed hash index is used to locate the target data block in the relational database; The located target data block is returned to the requirement analysis module.

[0013] Furthermore, in the mobile energy replenishment device based on a unified data platform described in this invention, the demand analysis module is also used for: Receive status data fed back by the execution module; Extract the actual refueling volume from multiple refueling operations from the received status data to form a historical actual refueling volume sequence; Obtain the historical target refueling volume sequence corresponding to the historical actual refueling volume sequence; Calculate the numerical difference between the corresponding positions in the historical actual refueling volume sequence and the historical target refueling volume sequence to generate a refueling volume deviation sequence; Analyze the refueling quantity deviation sequence, and when the absolute value of multiple consecutive deviation values ​​exceeds the preset tolerance, generate a weight adjustment factor; The generated weight adjustment factor is applied to the inventory balance constraint in the linear programming method to adjust the weight coefficient of the constraint in the objective function.

[0014] Furthermore, in the mobile energy replenishment device based on a unified data platform described in this invention, the control command module is also used for: After sending the data packet, start the timer; Listen for the confirmation response signal returned by the execution module; When an acknowledgment response signal is received before the timer expires, the acknowledgment response signal is parsed. Verify the integrity of the parsed confirmation response signal and the validity of the identity token; If no acknowledgment response signal is received before the timer expires, or if the identity token is found to be invalid or the signal is found to be incomplete, the retransmission counter is incremented. When the value of the retransmission counter is less than the maximum retransmission threshold, the transmission data packet is regenerated and sent to the execution module.

[0015] Secondly, the mobile energy replenishment system based on a unified data platform provided by the present invention is applied to the mobile energy replenishment device based on a unified data platform as described above, comprising: The data acquisition unit is used to obtain vehicle operating status parameters, energy demand signals, and supply facility status information, and output the acquired signals. The data integration unit is used to receive the acquisition signal output by the data acquisition unit, perform protocol conversion and format alignment on the acquisition signal, and generate a structured data stream; The demand analysis unit is used to receive the structured data stream generated by the data integration unit, process the structured data stream based on the algorithm model, and output the replenishment instruction parameters. The control command unit is used to receive the replenishment command parameters output by the demand analysis unit and encode the replenishment command parameters into control commands that can be recognized by the refueling machine. An execution unit is used to receive control commands encoded by the control command unit, drive the fuel dispenser to operate according to the control commands, and generate status data including the actual amount of fuel dispensed and feed it back to the data acquisition unit. The data acquisition unit, data integration unit, demand analysis unit, control instruction unit, and execution unit are connected in sequence, and the output of the execution unit is fed back to the data acquisition unit to form a closed-loop data flow.

[0016] Beneficial effects of this invention: This invention achieves automatic data flow and control through modular design, effectively solving the technical problems of low energy replenishment efficiency, high error rate, and difficult safety management caused by reliance on manual operation and data silos in existing mobile energy replenishment technologies. The data acquisition module acquires vehicle operating status parameters, energy demand signals, and replenishment facility status information in real time. The data integration module performs protocol conversion and format alignment on multi-source heterogeneous data through a unified data platform to generate a structured data stream. The demand analysis module processes the data stream based on an algorithm model and outputs intelligent replenishment strategies. The control command module encodes the strategies into safe and reliable control commands. The execution module drives the refueling machine to complete the automated replenishment operation and feeds back status data to the acquisition module to form a closed loop. This data-driven approach reduces the delays and errors introduced by manual intervention, improves replenishment efficiency, reduces the operational error rate through real-time data synchronization and intelligent decision-making, and enhances safety management capabilities through closed-loop monitoring. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the accompanying drawings without creative effort.

[0018] Figure 1 This is a system architecture diagram of the mobile energy supply device based on a unified data platform according to the present invention. Detailed Implementation

[0019] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0020] Please see Figure 1 In a first aspect, the mobile energy replenishment device based on a unified data platform provided by the present invention includes: The system comprises a data acquisition module, a data integration module, a requirements analysis module, a control command module, and an execution module. The data acquisition module obtains vehicle operating status parameters, energy demand signals, and supply facility status information, and outputs the acquired signals. The data integration module receives the acquisition signal output by the data acquisition module, performs protocol conversion and format alignment on the acquisition signal, and generates a structured data stream. The demand analysis module receives the structured data stream generated by the data integration module, processes the structured data stream based on the algorithm model, and outputs supply instruction parameters. The control command module receives the replenishment command parameters output by the demand analysis module and encodes the replenishment command parameters into control commands that the refueling machine can recognize. The execution module receives control commands encoded by the control command module, drives the fuel dispenser to operate according to the control commands, and generates status data including the actual amount of fuel dispensed, which is then fed back to the data acquisition module.

[0021] In intelligent fleet energy replenishment network management scenarios, mobile energy replenishment devices based on a unified data platform achieve automatic data flow and control through modular design. The data acquisition module integrates IoT sensors and network interfaces to acquire real-time vehicle operating status parameters such as fuel tank level and GPS coordinates, energy demand signals such as refueling requests from the fleet management platform, and replenishment facility status information such as fuel tank inventory and refueling machine operating status. Specifically, the onboard fuel tank level sensor outputs an analog voltage signal, the GPS locator generates digital location coordinates, the fuel tank inventory sensor provides a liquid level current signal, and the refueling machine encoder captures operating status pulse signals. The data acquisition module filters and converts the analog signals to digital signals, generating standard electrical signals, and then packages all signals and outputs the acquired signals via the RS-485 communication protocol. This step converts physical world parameters into a digital signal stream, laying the foundation for subsequent processing and reducing delays introduced by manual intervention.

[0022] The data integration module receives the acquisition signals output by the data acquisition module and connects them to a unified data platform for standardized processing. The acquisition signals use RS-485 communication protocol data packets, which the data integration module parses into TCP / IP data packets using a protocol converter. Subsequently, it performs format verification and removes outlier data points, such as filtering instantaneous fluctuations in tank levels. The cleaned data is converted into JSON format data objects, and after adding timestamp metadata tags, it is written to a relational database, generating a structured data stream with timestamp indexes. This process eliminates silos of heterogeneous data from multiple sources, enabling data interoperability and providing high-quality input for the analysis module.

[0023] The demand analysis module parses the structured data stream generated by the data integration module and processes vehicle location coordinates, fuel tank inventory, and fuel dispenser status parameters based on an algorithm model. The algorithm model uses a greedy algorithm to calculate the shortest path and combines it with linear programming for resource optimization and allocation, dynamically generating a replenishment task queue. For example, when multiple vehicles request refueling simultaneously, the model prioritizes vehicles with low fuel tank levels and balances fuel tank inventory with demand. The demand analysis module outputs replenishment command parameters, including the target refueling volume, vehicle execution priority, and safety threshold. These parameters support intelligent decision-making, replacing manual planning to improve response efficiency.

[0024] The control command module receives the refueling command parameters output by the demand analysis module and generates a binary coded operation sequence according to the fuel dispenser communication protocol. The sequence includes a preset refueling amount and solenoid valve control commands. The control command module uses the AES encryption algorithm to generate an authentication token and embeds it into the sequence. Using the MQTT protocol via the 4G communication module, the binary sequence with the embedded token is encapsulated into a transmission data packet, and a cyclic redundancy check (CRC) code is added before being sent to the execution module. This step enables command conversion from the cloud to the edge device, ensuring secure and reliable command delivery.

[0025] The execution module receives control commands encoded by the control instruction module, parses the binary encoded operation sequence to extract the preset refueling amount and solenoid valve control commands. The execution module drives the fuel dispenser controller to trigger the nozzle-lifting detection sensor and control the solenoid valve opening. During refueling, it monitors the real-time refueling amount; when the preset value is reached, it generates a shut-off command to close the solenoid valve. After completion, the payment is deducted via an IC card reader, and the actual refueling amount, timestamp, and settlement information are fed back as status data to the data acquisition module. This automated process reduces human error and improves refueling accuracy.

[0026] The status data generated by the execution module is fed back to the data acquisition module, forming a closed-loop data flow. For example, actual refueling volume data is used to iteratively optimize the algorithm model parameters of the demand analysis module, such as adjusting the weight of inventory balance constraints, thereby continuously improving system performance. The device achieves a seamless flow from acquisition to execution through data-driven processes, enhancing refueling efficiency, accuracy, and safety monitoring capabilities in intelligent fleet scenarios.

[0027] The data acquisition module obtains analog voltage signals through the vehicle's fuel tank level sensor. This sensor measures fuel level based on the principle of resistance change and outputs continuous voltage values. The GPS locator receives signals from the Global Positioning System and resolves the vehicle's latitude and longitude coordinates into digital signals, using the standard NMEA protocol format. The fuel tank inventory sensor uses ultrasonic detection technology to output a liquid level height current signal, with the current value proportional to the liquid level. The fuel dispenser encoder monitors the flow meter's operating status and generates pulse signals to indicate the number of refueling operations. The analog voltage signal and the liquid level height current signal are processed by a signal conditioning circuit, using a low-pass filter to eliminate environmental interference, and then converted into digital signals by an analog-to-digital converter, generating standard electrical signals such as TTL level. All signals, including standard electrical signals, vehicle position coordinate digital signals, and operating status pulse signals, are integrated by the microcontroller and packaged into a data frame structure. The data frame includes a timestamp and device identifier, and the acquired signals are output via the RS-485 communication protocol. The RS-485 bus supports differential transmission, improving anti-interference capabilities. This processing unifies multi-source heterogeneous signals into a digital format, providing structured input for the data integration module.

[0028] The data integration module receives the acquired signals, which are RS-485 protocol data packets containing multiple sensor data points. A protocol converter parses the RS-485 data packets into TCP / IP data packets for easy network transmission. The data within the TCP / IP data packets undergoes a format validation engine to verify data integrity, such as checksum matching, and removes abnormal data points whose values ​​exceed preset ranges, such as sudden fluctuations in fuel tank levels. The cleaned data is converted into JSON format data objects, which store parameters such as vehicle ID and fuel level values ​​using a key-value pair structure. A timestamp metadata tag is added to the JSON data objects; the timestamp is generated by the system clock and then written to a relational database using an SQL architecture, generating a structured data stream with timestamp indexes. This process achieves multi-protocol data fusion, eliminates information silos, and supports efficient querying by downstream modules.

[0029] The demand analysis module receives a structured data stream and parses vehicle location coordinates, fuel tank inventory, and fuel dispenser status parameters from it. The parser extracts key fields based on a database query language. Vehicle location coordinates are used for geospatial analysis, fuel tank inventory reflects resource availability, and fuel dispenser status parameters include busy / idle status. The parsed parameters are input into the algorithm model, which uses a greedy algorithm to calculate the shortest path. The greedy algorithm selects the nearest refueling point based on a distance-first principle and applies linear programming for resource optimization allocation. The linear programming considers inventory constraints and demand priorities, generating a replenishment task queue. The replenishment task queue is dynamically sorted, for example, prioritizing vehicles with low fuel levels. Based on the replenishment task queue output by the algorithm model, replenishment instruction parameters are generated, including the target refueling amount, vehicle execution priority, and a safety threshold. The safety threshold prevents overfilling risks. This analysis enables intelligent scheduling, replacing manual decision-making and improving response speed.

[0030] The control command module receives refueling command parameters, including the target refueling amount and priority. The command encoder generates a binary coded operation sequence based on the refueling command parameters and the communication protocol with the refueling machine. This binary sequence includes an operation code and parameter values, such as a preset refueling amount. An authentication token is generated using the AES encryption algorithm. AES provides symmetric encryption, and the token is embedded in the binary coded operation sequence to enhance command security. The binary coded operation sequence with the embedded authentication token is encapsulated into a transmission data packet via a 4G communication module using the MQTT protocol. The MQTT protocol supports publish-subscribe patterns, adapting to mobile environments. A cyclic redundancy check (CRC) code is added to the transmission data packet to detect transmission errors and send it to the execution module. This step ensures reliable command delivery and reduces communication failures.

[0031] The execution module receives a binary-coded operation sequence transmitted from the control command module. This sequence includes a preset refueling volume and solenoid valve control commands. The parser decodes the binary sequence, extracting the operation code and parameter values. For example, the preset refueling volume is stored in a fixed-point format, and the solenoid valve control commands include the opening percentage. When the fuel dispenser controller is activated, it triggers the nozzle-lifting detection sensor, which detects the nozzle lifting status via photoelectric sensing. The solenoid valve opening is controlled using a pulse-width modulation (PWM) signal to adjust the valve opening, achieving precise fuel flow control. During real-time refueling volume monitoring, the flow measurement converter converts the flow pulses into digital quantities, comparing them with the preset value in real time. When the real-time refueling volume reaches the preset value, a shut-off command is generated to close the solenoid valve and stop the refueling process. After the solenoid valve closes, the IC card reader reads the vehicle's IC card information, completes the payment settlement, and obtains the actual refueling volume, timestamp, and settlement information. Status data is fed back to the data acquisition module, forming a data loop and supporting system iterative optimization.

[0032] The unified data platform receives structured data streams with timestamped indexes written by the data integration module; these streams are stored in a relational database. When building a distributed hash index, the index engine maps data keys, such as vehicle IDs, to distributed storage nodes, improving query efficiency. The requirements analysis module sends data query requests, including query conditions such as time ranges or device identifiers. The unified data platform uses the distributed hash index to locate the target data block; the index calculates a hash value to determine the data storage location. After location, the target data block is returned to the requirements analysis module, enabling rapid data access and supporting real-time analysis and decision-making.

[0033] The demand analysis module receives status data from the execution module, including the actual refueling volume from multiple refueling operations. The module extracts the actual refueling volumes to form a historical actual refueling volume sequence, and simultaneously obtains the corresponding historical target refueling volume sequence, derived from previous refueling command parameters. It calculates the numerical difference between the historical actual refueling volume sequence and the historical target refueling volume sequence, generating a refueling volume deviation sequence. When analyzing the deviation sequence, a sliding window is used to detect consecutive deviation values. When the absolute value of multiple consecutive deviation values ​​exceeds a preset tolerance, a weight adjustment factor is generated. This weight adjustment factor is applied to the inventory balance constraint in the linear programming method, adjusting the weight coefficients of the constraint in the objective function to optimize the resource allocation strategy.

[0034] After sending the transmission data packet, the control instruction module starts a timer with a timeout threshold. The module listens for the acknowledgment signal returned by the execution module, which includes a sequence number and status information. If an acknowledgment signal is received before the timer expires, the signal content is parsed; integrity is verified using a Cyclic Redundancy Check (CRC) code, and the validity of the identity token is verified using the AES decryption algorithm. If no acknowledgment signal is received before the timer expires, or if the identity token is found to be invalid, or if the signal is found to be incomplete, the retransmission counter is incremented. The retransmission counter records the number of attempts; when the retransmission counter value is less than the maximum retransmission threshold, the transmission data packet is regenerated and sent to the execution module, ensuring reliable instruction transmission.

[0035] In the requirements analysis module, the algorithm model applies a greedy algorithm to calculate the shortest path. The greedy algorithm approximates the global optimum by iteratively selecting the current optimal solution. Specifically, for the vehicle set... Meet at the gas station The greedy algorithm is based on the distance matrix between the vehicle's location coordinates and the refueling point's location. Perform path selection. The algorithm steps are as follows: Step 1: For each vehicle From the distance matrix Choose the refueling point with the shortest distance. ,Right now .

[0036] Step Two: Move the vehicle Assigned to gas stations And update the available resources at the refueling points.

[0037] Step 3: Repeat steps 1 and 2 until all vehicles have been assigned.

[0038] in, Indicates the first Vehicles, For vehicle indexing, Indicates the first One gas station For gas station indexes, Indicates vehicle Arrive at the gas station Euclidean distance, This indicates the total number of vehicles. This indicates the total number of gas stations. This represents the index of the minimum value. This represents the index of the refueling point corresponding to the minimum distance.

[0039] The algorithm model also applies linear programming for resource optimization allocation. The objective function of the linear programming model is to minimize the total transportation cost, and the constraints include fuel tank inventory, vehicle demand, and refueling point capacity. Let... Indicates from the gas station To the vehicle Fuel distribution amount Indicates from the gas station To the vehicle The unit transportation cost Indicates a refueling point Inventory levels Indicates vehicle The demand. The linear programming model is expressed as: ; The constraints are: ; ; ; in, Indicates from the gas station To the vehicle Fuel distribution amount Indicates from the gas station To the vehicle The unit transportation cost Indicates a refueling point Inventory levels Indicates vehicle The demand, This indicates the total number of vehicles. This indicates the total number of gas stations. This represents the summation operation. Indicates "for all". This represents minimizing the objective function.

[0040] The demand analysis module also includes the analysis of refueling volume deviation sequences. The historical actual refueling volume sequence is denoted as... The historical target refueling volume sequence is recorded as follows: Fueling volume deviation sequence Generate by calculating the difference at corresponding positions: ; in, Indicates the first The actual amount of fuel dispensed in each refueling operation. Indicates the first The target amount of fuel to be refueled in this refueling operation. Indicates the first Deviation value of the refueling operation This represents the total number of historical data points. It means "for all".

[0041] When analyzing the deviation sequence, a sliding window is used to detect continuous deviation values. Let the window size be... For each window position, calculate the sum of the absolute values ​​of the deviations within the window. If the absolute values ​​of multiple consecutive deviations exceed a preset tolerance... At that time, a weight adjustment factor is generated. Specifically, for the in-window bias sequence If all for Then the weight adjustment factor is generated. : ; in, Indicates the weight adjustment factor. Indicates the scaling factor. Indicates the size of the sliding window. Indicates the first Deviation value of the refueling operation This indicates the preset tolerance threshold. This represents the summation operation. This represents the absolute value of the deviation. Indicates the starting position index of the window.

[0042] The generated weight adjustment factor Inventory balance constraints applied in linear programming methods. Original constraints. Adjusted to This means that the weighting coefficients of the constraints in the objective function are adjusted.

[0043] in, Indicates from the gas station To the vehicle Fuel distribution amount Indicates a refueling point Inventory levels Indicates the weight adjustment factor. This indicates the total number of vehicles. This indicates a summation operation.

[0044] The data processing path for the above calculations is as follows: The requirements analysis module parses vehicle location coordinates, fuel tank inventory, and fuel dispenser status parameters from the structured data stream. After inputting these into the algorithm model, it first calculates the shortest path using a greedy algorithm, then optimizes resource allocation using a linear programming method to generate a supply task queue. Simultaneously, based on the status data fed back from the execution module, it calculates the refueling volume deviation sequence, analyzes statistical characteristics, and generates a weight adjustment factor to adjust the constraints of the linear programming model, achieving dynamic optimization.

[0045] Secondly, the mobile energy replenishment system based on a unified data platform provided by the present invention is applied to the mobile energy replenishment device based on a unified data platform as described above, comprising: The data acquisition unit is used to obtain vehicle operating status parameters, energy demand signals, and supply facility status information, and output the acquired signals. The data integration unit is used to receive the acquisition signal output by the data acquisition unit, perform protocol conversion and format alignment on the acquisition signal, and generate a structured data stream; The demand analysis unit is used to receive the structured data stream generated by the data integration unit, process the structured data stream based on the algorithm model, and output the replenishment instruction parameters. The control command unit is used to receive the replenishment command parameters output by the demand analysis unit and encode the replenishment command parameters into control commands that can be recognized by the refueling machine. An execution unit is used to receive control commands encoded by the control command unit, drive the fuel dispenser to operate according to the control commands, and generate status data including the actual amount of fuel dispensed and feed it back to the data acquisition unit. The data acquisition unit, data integration unit, demand analysis unit, control instruction unit, and execution unit are connected in sequence, and the output of the execution unit is fed back to the data acquisition unit to form a closed-loop data flow.

[0046] Based on the scenario of intelligent fleet energy replenishment network management, the device of this invention achieves automatic data flow through modular design. The data acquisition module integrates IoT sensors: the on-board fuel tank level sensor measures the fuel level based on the principle of resistance change and outputs an analog voltage signal; the GPS locator analyzes satellite signals to generate digital signals of vehicle latitude and longitude coordinates; the fuel tank inventory sensor uses ultrasonic technology to output a liquid level height current signal; and the fuel dispenser encoder captures the working status pulse signal. The signals are filtered and converted from analog to digital by the signal conditioning circuit to generate a standard electrical signal, which is then packaged with the refueling request digital signal issued by the fleet management platform and output as the acquired signal via RS-485 communication protocol.

[0047] After receiving the acquired signals, the data integration module uses a protocol converter to parse RS-485 data packets into TCP / IP data packets. The data cleaning engine verifies data integrity and removes outliers, such as filtering out instantaneous fluctuations in tank levels. The cleaned data is then converted into JSON format data objects, with timestamp metadata tags generated by the system clock added. This data is then written to a relational database to generate a structured data stream with timestamp indexes. This process eliminates information silos through a centralized storage architecture based on a unified data foundation.

[0048] The requirements analysis module parses the structured data stream, extracting vehicle location coordinates, fuel tank inventory, and fuel dispenser status parameters. The algorithm model uses a greedy algorithm to calculate the shortest path and combines it with linear programming to optimize resource allocation, such as prioritizing vehicles with low fuel levels and balancing inventory constraints, dynamically generating a replenishment task queue. Based on the queue, it outputs replenishment command parameters such as the target refueling amount, vehicle execution priority, and safety threshold.

[0049] After receiving the refueling command parameters, the control command module instructs the encoder to generate a binary encoded operation sequence according to the refueling machine communication protocol, embedding an authentication token generated by the AES encryption algorithm. The data packets are then encapsulated and transmitted via the 4G communication module using the MQTT protocol, with a cyclic redundancy check (CRC) code added before being sent to the execution module.

[0050] The execution module parses the binary sequence to extract the preset refueling amount and the solenoid valve control command, driving the fuel dispenser controller to trigger the nozzle-lifting detection sensor, which then controls the solenoid valve opening to adjust the fuel flow. The flow measurement converter monitors the refueling amount in real time, closing the solenoid valve when the preset value is reached, and the IC card reader automatically completes the payment settlement. The actual refueling amount, timestamp, and settlement information are fed back as status data to the data acquisition module, forming a closed-loop data stream.

[0051] The unified data platform accelerates query response through distributed hash indexes. The demand analysis module periodically analyzes historical refueling deviation sequences, adjusting the inventory constraint weights of the linear programming model when consecutive deviations exceed the tolerance. The control command module uses timers to listen for and confirm responses; if no valid response is received within the timeout period, a retransmission mechanism is triggered to ensure the reliability of command transmission. The device achieves automated workflow from data acquisition to execution through data-driven processes, effectively improving refueling efficiency, reducing human error rates, and enhancing safety management capabilities.

[0052] In the data acquisition module, the vehicle-mounted fuel tank level sensor uses a resistive sensor, outputting a 0-5V analog voltage signal, corresponding to a fuel tank level of 0-100%; the GPS locator uses the NMEA-0183 protocol to output digital latitude and longitude coordinates; the fuel tank inventory sensor uses an ultrasonic sensor, outputting a 4-20mA current signal, corresponding to a fuel level of 0-10 meters; the fuel dispenser encoder outputs TTL level pulse signals, with each pulse representing 0.1 liters of flow. The signal conditioning circuit uses a second-order Butterworth low-pass filter with a cutoff frequency of 10Hz to eliminate high-frequency noise; the analog-to-digital converter uses 12-bit resolution and a reference voltage of 5V to convert analog signals into digital values. The microcontroller uses an ARM Cortex-M4 core, and after acquiring all signals, it packages them into data frames with a frame header of 0xAA and a frame tail of 0x55, containing the device ID, timestamp, and sensor data, and outputs them through an RS-485 interface with a baud rate of 9600bps, 8 data bits, 1 stop bit, and no parity check.

[0053] In the data integration module, the protocol converter uses the MAX485 chip to convert RS-485 signals to TTL levels. Then, the ESP32 microcontroller parses the data frames, extracts valid data, and encapsulates it into TCP / IP packets. The target IP address is the unified data base server. The format validation engine checks the data range: fuel tank level must be between 0 and 100; GPS coordinate latitude must be between -90 and 90 degrees; longitude must be between -180 and 180 degrees; and the liquid level height current must correspond to 4-20mA. Data points outside these ranges are marked as invalid and discarded. JSON format data objects use a key-value pair structure, for example, {"vehicle_id": "V001", "fuel_level": 75, "timestamp": "2026-02-04T10:30:00Z"}. The timestamp is generated by the system clock and uses the ISO 8601 format. The relational database uses MySQL, and the table structure includes fields such as id, timestamp, device_id, and sensor_value. The distributed hash index uses a consistent hashing algorithm to map data key-value pairs to three storage nodes, and the index key is a combination of device ID and timestamp.

[0054] In the demand analysis module, the algorithm model is implemented using the Python programming language. A greedy algorithm iterates through the vehicle set V and the gas station set P, calculates the Euclidean distance matrix D, and performs matrix operations using the NumPy library. Each time, it selects the gas station with the shortest distance to allocate vehicles and updates the gas station inventory. The linear programming model uses the `linprog` function from the SciPy library. The objective function coefficients `c_ij` are set based on distance cost. Constraints include the inventory cap `s_j` and the demand equation `d_i`. The solver uses the simplex method to generate allocation schemes. The sliding window size `w` is set to 5, the preset tolerance `τ` is set to 0.5 liters, the weight adjustment factor `α` is calculated using the moving average of the historical deviation sequence, and the scaling factor `β` is set to 0.1. The adjusted inventory constraints are treated as soft constraints in the linear programming model, with the weight coefficients increased by a factor of `α`.

[0055] In the control command module, the command encoder generates a binary operation sequence according to the refueling pump communication protocol DL / T 645-2007. Operation code 0x11 indicates setting the refueling volume, with parameters being 4 bytes of fixed-point numbers. The authentication token uses the AES-256 encryption algorithm, with a 256-bit key length and CBC encryption mode. The initialization vector is randomly generated, and the token is embedded in reserved fields of the sequence. The 4G communication module uses the SIM7600 chip, with the MQTT protocol topic set to " / refuel / command", the broker address being mqtt.broker.example.com, and CRC-16 checksums added to transmitted data packets with a polynomial of 0x8005. The timer is set to a 5-second timeout threshold. The acknowledgment response signal includes a sequence number and status bytes, and the same CRC-16 algorithm and AES decryption are used for verification. The retransmission counter has a maximum threshold of 3 times, and a new encryption token is generated for each retransmission.

[0056] In the execution module, the parser decodes the binary sequence, extracting the preset refueling amount as a 32-bit unsigned integer in units of 0.1 liters. The solenoid valve control command is a PWM signal with a frequency of 1kHz and a duty cycle of 0-100% corresponding to the valve opening. The fuel dispenser controller uses an STM32F103 chip. The nozzle-lifting detection sensor detects the nozzle position via an optocoupler. The solenoid valve drive circuit uses a MOSFET switch. Real-time refueling amount monitoring is achieved through pulse counting by the flow meter, with each pulse representing 0.1 liters. The comparator outputs a shut-off signal when the count reaches the preset value. The IC card reader supports the ISO 7816 protocol, reads the vehicle's IC card ID, and deductions are completed through a backend API interface. The actual refueling amount, timestamp, and settlement information are fed back in JSON format and sent to the IP address of the data acquisition module via a Wi-Fi module.

[0057] In the data acquisition module, the vehicle-mounted fuel tank level sensor uses a resistive sensor, outputting a 0-5V analog voltage signal, corresponding to a fuel tank level of 0-100%; the GPS locator uses the NMEA-0183 protocol to output digital latitude and longitude coordinates; the fuel tank inventory sensor uses an ultrasonic sensor, outputting a 4-20mA current signal, corresponding to a fuel level of 0-10 meters; the fuel dispenser encoder outputs TTL level pulse signals, with each pulse representing 0.1 liters of flow. The signal conditioning circuit uses a second-order Butterworth low-pass filter with a cutoff frequency of 10Hz to eliminate high-frequency noise; the analog-to-digital converter uses 12-bit resolution and a reference voltage of 5V to convert analog signals into digital values. The microcontroller uses an ARM Cortex-M4 core, and after acquiring all signals, it packages them into data frames with a frame header of 0xAA and a frame tail of 0x55, containing the device ID, timestamp, and sensor data, and outputs them through an RS-485 interface with a baud rate of 9600bps, 8 data bits, 1 stop bit, and no parity check.

[0058] In the data integration module, the protocol converter uses a MAX485 chip to convert RS-485 signals to TTL levels. Then, an ESP32 microcontroller parses the data frames, extracts valid data, and encapsulates it into TCP / IP packets. The target IP address is the unified data base server. The format validation engine checks the data range: the tank level must be between 0 and 100; the GPS coordinate latitude range is -90 to 90 degrees; the longitude range is -180 to 180 degrees; and the liquid level height current value must correspond to 4-20mA. Data points outside these ranges are marked as invalid and discarded. JSON format data objects use a key-value pair structure, for example; The table name is {"vehicle_id":"V001","fuel_level":75,"timestamp":"2026-02-04T10:30:00Z"}. The timestamp is generated by the system clock and uses a preset standard format. The relational database used is MySQL. The table structure includes fields such as id, timestamp, device_id, and sensor_value. The distributed hash index uses a consistent hashing algorithm to map data key-value pairs to three storage nodes. The index key is a combination of the device ID and the timestamp.

[0059] In the demand analysis module, the algorithm model is implemented using the Python programming language. A greedy algorithm iterates through the vehicle set V and the gas station set P, calculates the Euclidean distance matrix D, and performs matrix operations using the NumPy library. Each time, it selects the gas station with the shortest distance to allocate vehicles and updates the gas station inventory. The linear programming model uses the `linprog` function from the SciPy library. The objective function coefficients `c_ij` are set based on distance cost. Constraints include the inventory cap `s_j` and the demand equation `d_i`. The solver uses the simplex method to generate allocation schemes. The sliding window size `w` is set to 5, the preset tolerance `τ` is set to 0.5 liters, the weight adjustment factor `α` is calculated using the moving average of the historical deviation sequence, and the scaling factor `β` is set to 0.1. The adjusted inventory constraints are treated as soft constraints in the linear programming model, with the weight coefficients increased by a factor of `α`.

[0060] In the control command module, the command encoder generates a binary operation sequence according to the refueling pump communication protocol DL / T 645-2007. Operation code 0x11 indicates setting the refueling volume, with parameters being 4 bytes of fixed-point numbers. The authentication token uses the AES-256 encryption algorithm, with a 256-bit key length and CBC encryption mode. The initialization vector is randomly generated, and the token is embedded in reserved fields of the sequence. The 4G communication module uses the SIM7600 chip, with the MQTT protocol topic set to " / refuel / command", the broker address being mqtt.broker.example.com, and CRC-16 checksums added to transmitted data packets with a polynomial of 0x8005. The timer is set to a 5-second timeout threshold. The acknowledgment response signal includes a sequence number and status bytes, and the same CRC-16 algorithm and AES decryption are used for verification. The retransmission counter has a maximum threshold of 3 times, and a new encryption token is generated for each retransmission.

[0061] In the execution module, the parser decodes the binary sequence, extracting the preset refueling amount as a 32-bit unsigned integer in units of 0.1 liters. The solenoid valve control command is a PWM signal with a frequency of 1kHz and a duty cycle of 0-100% corresponding to the valve opening. The fuel dispenser controller uses an STM32F103 chip. The nozzle-lifting detection sensor detects the nozzle position via an optocoupler. The solenoid valve drive circuit uses a MOSFET switch. Real-time refueling amount monitoring is achieved through pulse counting by the flow meter, with each pulse representing 0.1 liters. The comparator outputs a shut-off signal when the count reaches the preset value. The IC card reader supports the ISO 7816 protocol, reads the vehicle's IC card ID, and deductions are completed through a backend API interface. The actual refueling amount, timestamp, and settlement information are fed back in JSON format and sent to the IP address of the data acquisition module via a Wi-Fi module.

[0062] In the unified data infrastructure, the distributed hash index uses a consistent hashing algorithm to map data key-value pairs, such as vehicle IDs and timestamps, to physical storage nodes. The index structure includes hash buckets and pointers, supporting fast queries. When the requirements analysis module sends a data query request, the unified data infrastructure calculates the hash value of the query conditions, locates the corresponding storage node, retrieves the target data block, and returns the result, with a query response time of less than 100 milliseconds.

[0063] In the algorithm implementation of the demand analysis module, the greedy algorithm creates an empty allocation list during initialization, iterates through the vehicle set and sorts them in ascending order of fuel tank level. For each vehicle, it calculates the distance to all available refueling points, selects the refueling point with the shortest distance and sufficient inventory, and updates the refueling point inventory after allocation. When constructing the linear programming model, the decision variable x_ij is defined as a continuous variable, the objective function is to minimize the total distance cost, and the constraints include inventory constraints and demand constraints. After solving, an allocation scheme is generated. The weight adjustment factor α is calculated based on the historical deviation sequence. When the absolute value of five consecutive deviation values ​​exceeds 0.5 liters, α is taken as the average of the deviation sequence multiplied by 0.1, adjusting the inventory constraint weight of the linear programming model.

[0064] In the communication process of the control command module, after the data packet is sent, a hardware timer is started. The timer count increments from 0, and an acknowledgment response signal is checked every millisecond. The acknowledgment response signal includes a sequence number, a status code, and a CRC checksum. After parsing, the sequence number is verified to match and the status code is 0x00. If the AES decrypted identity token matches, it is considered valid. The retransmission mechanism is triggered when a timeout occurs or the verification fails. The retransmission counter is initially 0 and increments by 1 with each retransmission. The retransmitted data packet retains the original sequence number but updates the encryption token. The maximum number of retransmissions is 3.

[0065] In the refueling control of the execution module, the solenoid valve opening control adopts a PID algorithm with a proportional coefficient Kp=0.8, an integral coefficient Ki=0.1, and a derivative coefficient Kd=0.05. The PWM duty cycle is adjusted according to the deviation between the real-time flow rate and the target flow rate. The flow meter pulse input is fed to a counter, which samples the value every 100 milliseconds and compares it with a preset value. When the sampled value is greater than or equal to the preset value, an interrupt signal is generated, and the solenoid valve is closed. The IC card reader sends a deduction request to the backend server. The server verifies the card number's validity, deducts the amount, and returns a transaction success signal.

[0066] At the system level, the data acquisition unit, data integration unit, demand analysis unit, control command unit, and execution unit are connected via an Ethernet switch. Inter-unit communication uses the TCP protocol on port 8000. The data acquisition unit periodically collects sensor data every 1 second. The data integration unit processes the input data in real time. The demand analysis unit runs an algorithm model every 5 seconds. The control command unit sends control commands asynchronously, and the execution unit executes actions synchronously and provides feedback. In the closed-loop data flow, the output of the execution unit is routed to the input buffer of the data acquisition unit via the network switch. The data acquisition unit parses the feedback data and updates its internal state.

[0067] Embodiment 1 of the present invention; In a logistics fleet management scenario, when the fleet management platform receives a refueling request signal, the data acquisition module collects analog voltage signals through the vehicle's fuel tank level sensor, generates digital signals of vehicle location coordinates through the GPS locator, and simultaneously monitors the liquid level current signal through the fuel tank inventory sensor. The signals are filtered and converted from analog to digital by a signal conditioning circuit to generate a standard electrical signal, which is then packaged with the working status pulse signal of the fuel dispenser encoder and output as the acquired signal via the RS-485 communication protocol. The data integration module parses the RS-485 protocol data packets into TCP / IP data packets, performs format verification and outlier removal, converts them into JSON format data objects, adds timestamp tags, and stores them in a relational database, forming a structured data stream with timestamp indexes. The requirements analysis module parses vehicle location, inventory level, and equipment status parameters from the structured data stream, calculates the nearest refueling point using a greedy algorithm, generates a replenishment task queue using linear programming, and outputs replenishment command parameters including the target refueling amount and safety threshold. The control command module encodes the parameters into a binary operation sequence, embeds an AES encrypted token, and sends it via the MQTT protocol over a 4G network. The execution module parses the sequence to control the opening of the solenoid valve, and automatically closes the valve when the refueling amount reaches the preset value. After the IC card reader completes the settlement, it feeds back the actual refueling amount data to the acquisition module, forming a closed-loop control.

[0068] Embodiment 2 of the present invention; In the public transportation energy replenishment scenario, the data acquisition module simultaneously acquires fuel tank level signals from multiple buses, refueling request signals from the dispatch platform, and gas station inventory status signals. The data integration module performs protocol conversion on the multi-source data using a unified data platform, unifying CAN bus signals and network interface data into TCP / IP format. After data cleaning, a structured data stream with a distributed hash index is generated. The demand analysis module uses a sliding window algorithm to analyze historical refueling deviation sequences. When continuous deviations exceed the tolerance threshold, the inventory balance constraint weights in the linear programming model are dynamically adjusted. The control command module starts a timer after sending the binary encoded operation sequence. If no confirmation response is received from the execution module within the timeout threshold, a retransmission mechanism is triggered to resend the encrypted data packet. The execution module calibrates the refueling volume in real time using a flow measurement converter. After completing the refueling operation, it automatically uploads refueling error data to the cloud database. The demand analysis module optimizes the parameter settings of the algorithm model based on the error data, achieving continuous improvement of the replenishment strategy. This design enables the system to automatically balance resource allocation across multiple refueling points during peak hours, while continuously correcting operational errors through a closed-loop feedback mechanism.

Claims

1. A mobile energy replenishment device based on a unified data platform, characterized in that, include: The system comprises a data acquisition module, a data integration module, a requirements analysis module, a control command module, and an execution module. The data acquisition module obtains vehicle operating status parameters, energy demand signals, and supply facility status information, and outputs the acquired signals. The data integration module receives the acquisition signal output by the data acquisition module, performs protocol conversion and format alignment on the acquisition signal, and generates a structured data stream. The demand analysis module receives the structured data stream generated by the data integration module, processes the structured data stream based on the algorithm model, and outputs supply instruction parameters. The control command module receives the replenishment command parameters output by the demand analysis module and encodes the replenishment command parameters into control commands that the refueling machine can recognize. The execution module receives control commands encoded by the control command module, drives the fuel dispenser to operate according to the control commands, and generates status data including the actual amount of fuel dispensed, which is then fed back to the data acquisition module.

2. The mobile energy replenishment device based on a unified data platform according to claim 1, characterized in that, The data acquisition module is also used for: The analog voltage signal is obtained through the vehicle-mounted fuel tank level sensor, the digital signal of the vehicle position coordinates is obtained through the GPS locator, the liquid level current signal is obtained through the fuel tank inventory sensor, and the working status pulse signal is obtained through the fuel dispenser encoder. The analog voltage signal and the liquid level height current signal are filtered and converted from analog to digital to generate a standard electrical signal; The standard electrical signal, the vehicle position coordinate digital signal, and the working status pulse signal are packaged together and output as the acquisition signal through the RS-485 communication protocol.

3. The mobile energy replenishment device based on a unified data platform according to claim 2, characterized in that, The data integration module is also used for: Receive the acquisition signal, wherein the acquisition signal is a data packet using the RS-485 communication protocol; The RS-485 communication protocol data packets are parsed into TCP / IP data packets; Perform format verification on the data within the TCP / IP data packets and remove abnormal data points whose values ​​exceed a preset range; Convert the validated and rejected data into JSON format data objects; After adding timestamp metadata tags to the JSON format data object, it is written to a relational database, generating a structured data stream with timestamp indexes and outputting it.

4. The mobile energy replenishment device based on a unified data platform according to claim 3, characterized in that, The requirements analysis module is also used for: Receive the structured data stream; The vehicle location coordinates, fuel tank inventory, and fuel dispenser status parameters are parsed from the structured data stream. The parsed vehicle location coordinates, fuel tank inventory, and fuel dispenser status parameters are input into the algorithm model; the algorithm model uses a greedy algorithm to calculate the shortest path and a linear programming method to optimize resource allocation and generate a supply task queue. Based on the supply task queue output by the algorithm model, supply instruction parameters including target refueling amount, vehicle execution priority, and safety threshold are generated.

5. The mobile energy replenishment device based on a unified data platform according to claim 4, characterized in that, The control command module is also used for: Receive the supply command parameters; Based on the refueling instruction parameters and the refueling machine communication protocol, a binary encoded operation sequence is generated; An authentication token is generated using the AES encryption algorithm, and the authentication token is embedded in the binary encoded operation sequence; The binary-coded operation sequence with embedded authentication tokens is encapsulated into a transmission data packet using a 4G communication module and the MQTT protocol. A cyclic redundancy check code is added to the transmitted data packet and sent to the execution module.

6. The mobile energy replenishment device based on a unified data platform according to claim 5, characterized in that, The execution module is also used for: Receive the control command, wherein the control command is a binary encoded operation sequence; The binary encoded operation sequence is analyzed to extract the preset refueling amount and the solenoid valve control command; According to the solenoid valve control command, the fuel dispenser controller is driven to trigger the nozzle lifting detection sensor and control the opening degree of the solenoid valve. During the refueling process, the real-time refueling volume is monitored, and when the real-time refueling volume reaches the preset refueling volume value, a shutdown command is generated to close the solenoid valve. After the solenoid valve is closed, the payment is deducted and settled through the IC card reader, and the actual amount of fuel, timestamp and settlement information are obtained. The actual refueling amount, timestamp, and settlement information are fed back to the data acquisition module as status data.

7. The mobile energy replenishment device based on a unified data platform according to claim 6, characterized in that, Also includes: The unified data base receives a structured data stream with a timestamp index written by the data integration module; Build a distributed hash index for the structured data stream written to the relational database; Receive data query requests sent by the requirements analysis module; Based on the data query request, the distributed hash index is used to locate the target data block in the relational database; The located target data block is returned to the requirement analysis module.

8. The mobile energy replenishment device based on a unified data platform according to claim 7, characterized in that, The requirements analysis module is also used for: Receive status data fed back by the execution module; Extract the actual refueling volume from multiple refueling operations from the received status data to form a historical actual refueling volume sequence; Obtain the historical target refueling volume sequence corresponding to the historical actual refueling volume sequence; Calculate the numerical difference between the corresponding positions in the historical actual refueling volume sequence and the historical target refueling volume sequence to generate a refueling volume deviation sequence; Analyze the refueling quantity deviation sequence, and when the absolute value of multiple consecutive deviation values ​​exceeds the preset tolerance, generate a weight adjustment factor; The generated weight adjustment factor is applied to the inventory balance constraint in the linear programming method to adjust the weight coefficient of the constraint in the objective function.

9. The mobile energy replenishment device based on a unified data platform according to claim 8, characterized in that, The control command module is also used for: After sending the data packet, start the timer; Listen for the confirmation response signal returned by the execution module; When an acknowledgment response signal is received before the timer expires, the acknowledgment response signal is parsed. Verify the integrity of the parsed confirmation response signal and the validity of the identity token; If no acknowledgment response signal is received before the timer expires, or if the identity token is found to be invalid or the signal is found to be incomplete, the retransmission counter is incremented. When the value of the retransmission counter is less than the maximum retransmission threshold, the transmission data packet is regenerated and sent to the execution module.

10. A mobile energy replenishment system based on a unified data platform, applied to the mobile energy replenishment device based on a unified data platform as described in any one of claims 1 to 9, characterized in that, include: The data acquisition unit is used to obtain vehicle operating status parameters, energy demand signals, and supply facility status information, and output the acquired signals. The data integration unit is used to receive the acquisition signal output by the data acquisition unit, perform protocol conversion and format alignment on the acquisition signal, and generate a structured data stream; The demand analysis unit is used to receive the structured data stream generated by the data integration unit, process the structured data stream based on the algorithm model, and output the replenishment instruction parameters. The control command unit is used to receive the replenishment command parameters output by the demand analysis unit and encode the replenishment command parameters into control commands that can be recognized by the refueling machine. An execution unit is used to receive control commands encoded by the control command unit, drive the fuel dispenser to operate according to the control commands, and generate status data including the actual amount of fuel dispensed and feed it back to the data acquisition unit. The data acquisition unit, data integration unit, demand analysis unit, control instruction unit, and execution unit are connected in sequence, and the output of the execution unit is fed back to the data acquisition unit to form a closed-loop data flow.

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