A method and system for analyzing total energy consumption of port-wide equipment per day

Abstract

This application relates to the field of port equipment energy consumption analysis technology, and discloses a method and system for analyzing the daily total energy consumption of all equipment in a port. The method includes: acquiring raw energy consumption data streams uploaded by heterogeneous equipment across the port; performing protocol parsing and time alignment on the data to generate standardized energy consumption time-series data; using the time-series data to drive a predefined port equipment energy consumption state machine, automatically dividing the data into operating, idle, and standby state sequences according to preset power characteristic rules for different equipment types; labeling the operating states with specific operational logic tags based on port operation plans and operation logs; constructing an energy flow model with a three-layer mapping of equipment, state, and energy consumption based on the labeled sequences; and finally aggregating and generating a daily total energy consumption data product that can be drilled down and traced by natural day. This invention solves the problems of heterogeneous, unclear, and disconnected port equipment energy consumption data from production operations, and achieves refined and traceable energy consumption analysis.

Description

Technical Field

[0001] This application relates to the field of energy consumption analysis technology for port equipment, and in particular to a method and system for analyzing the total daily energy consumption of all equipment in a port. Background Technology

[0002] Currently, ports, as logistics hubs, have a large number and variety of large-scale equipment (such as quay cranes, yard cranes, and container trucks), resulting in huge total energy consumption and inefficient management. Achieving refined energy consumption management is key to green port operation and cost control. However, existing technologies face significant challenges in analyzing the total energy consumption of all port equipment.

[0003] First, port equipment originates from different manufacturers, and their internal control and monitoring systems employ varying industrial communication protocols, resulting in inconsistent energy consumption data formats and standards. This makes unified collection and aggregation difficult, creating "data silos." Second, the data directly obtained from equipment is typically low-level signals such as instantaneous power, which do not directly reflect the actual operating status of the equipment. For example, when equipment is operating at high efficiency, idling inefficiently, or standing still, its power performance may only differ quantitatively without qualitative characteristics. This makes it impossible for managers to effectively distinguish between effective operational energy consumption and unnecessary idle or standby energy consumption from the energy consumption data. Consequently, energy consumption analysis remains at the level of total statistics and cannot support in-depth energy-saving optimization. Finally, the physical energy consumption data of the equipment is isolated from the port's production operation systems (such as work plans and ship information). Even if the energy consumption for a certain period is known, it is impossible to accurately answer the core management question: "Which ship is this energy consumption specifically used to serve, and what loading and unloading process is being performed?" In other words, energy consumption cannot be linked to specific production tasks, causing energy consumption analysis to be detached from production reality and failing to serve decisions on improving operational efficiency and optimizing resource scheduling. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of this application provide a method for analyzing the daily total energy consumption of all equipment in a port to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this application provides a method for analyzing the daily total energy consumption of all equipment in a port, including:

[0006] Acquire raw energy consumption data streams uploaded by various heterogeneous devices across the port through their respective industrial control networks;

[0007] The original energy consumption data stream is subjected to protocol parsing and time alignment processing to generate energy consumption time-series data with a unified timestamp and standardized device identifier;

[0008] The energy consumption time series data is input into a predefined port equipment energy consumption state machine. By matching power characteristic rules, the energy consumption time series data is divided into energy consumption state sequences corresponding to different physical states, wherein the physical states include at least the operating state, the idle state, and the standby state.

[0009] Based on the port operation plan and equipment operation log, operation logic labels are marked for the operation status in the energy consumption status sequence to obtain the labeled status sequence;

[0010] Based on the labeled state sequence, a three-layer energy flow model mapping device-state-energy consumption is constructed;

[0011] Based on all state energy consumption units in the energy flow model, aggregate calculations are performed according to natural day time windows to generate port daily total energy consumption data products.

[0012] To address the aforementioned problems, this application also provides a daily total energy consumption analysis system for all equipment in a port, the system comprising:

[0013] The data acquisition and aggregation module is used to acquire raw energy consumption data streams uploaded by various heterogeneous devices throughout the port through their respective industrial control networks;

[0014] The data preprocessing and standardization module is used to perform protocol parsing and time alignment processing on the raw energy consumption data stream to generate energy consumption time-series data with a unified timestamp and standardized device identifier;

[0015] The energy consumption status identification module is used to input the energy consumption time series data into a predefined port equipment energy consumption status machine, and divide the energy consumption time series data into energy consumption status sequences corresponding to different physical states by matching power feature rules, wherein the physical states include at least operating state, idle state and standby state.

[0016] The operation logic fusion module is used to label the operation status in the energy consumption status sequence with operation logic tags based on the port operation plan and equipment operation log, so as to obtain the labeled status sequence.

[0017] The energy flow modeling module is used to construct a three-layer energy flow model that maps devices, states, and energy consumption based on the labeled state sequence.

[0018] The multi-dimensional aggregation and traceability analysis module is used to aggregate and calculate based on all state energy consumption units in the energy flow model according to the natural day time window, and generate port daily total energy consumption data products.

[0019] This invention solves three core challenges: data heterogeneity, state ambiguity, and business isolation. It realizes the transformation from raw signals to management decision-making knowledge, and ultimately generates a traceable and drill-down daily total energy consumption data product.

[0020] First, this method achieves unified and accurate interpretation and status identification of energy consumption data from heterogeneous equipment across the entire site at the physical level. By deploying a protocol parsing gateway and performing standardized preprocessing, the raw energy consumption data streams from multiple heterogeneous sources are transformed into standardized time-series data with a unified time base and equipment identifier, overcoming data access barriers. Furthermore, it creatively applies predefined energy consumption state machines for port equipment tailored to the physical characteristics of different types of equipment. By matching composite rules including power thresholds, trends, and durations, continuous power time-series data is automatically and objectively divided into sequences of operating states, idle states, and standby states with clear physical meanings. This enables the system to accurately separate and quantify effective operational energy consumption from various ineffective or inefficient energy consumption, providing unprecedented granularity for energy consumption assessment and optimization.

[0021] Secondly, this method achieves deep correlation and transparent traceability between energy consumption data and production activities at the business level. By intelligently comparing and labeling the time information of work instructions in the work plan and operation log with the aforementioned physical state sequence, specific logical labels are assigned to the work status, thus firmly linking the physical fact of "equipment consuming energy" with the business fact of "why energy is being consumed." Based on this, the constructed equipment-status-energy consumption three-layer mapping energy flow model, and the daily total energy consumption data product generated accordingly, not only provide multi-dimensional summary statistics, but more importantly, retain the complete correlation path for drilling down from any summary result to the specific equipment, specific status, and specific work task at the bottom level. This enables managers not only to grasp the overall daily energy consumption trend across the entire site, all categories, and all statuses, but also to quickly locate energy consumption anomalies and trace them back to specific equipment and specific work processes for root cause analysis. Attached Figure Description

[0022] Figure 1 A flowchart illustrating a method for analyzing the daily total energy consumption of all equipment in a port, provided as an embodiment of this application;

[0023] Figure 2 A functional block diagram of a port's total daily energy consumption analysis system for all equipment, provided in an embodiment of this application;

[0024] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0026] This application provides a method for analyzing the daily total energy consumption of all equipment in a port. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0027] Reference Figure 1 The diagram shown is a flowchart illustrating a method for analyzing the daily total energy consumption of all equipment in a port according to an embodiment of this application. In this embodiment, the method for analyzing the daily total energy consumption of all equipment in a port includes:

[0028] S1. Obtain raw energy consumption data streams uploaded by various heterogeneous devices throughout the port through their respective industrial control networks.

[0029] In some embodiments, acquiring raw energy consumption data streams uploaded by various heterogeneous devices across the port through their respective industrial control networks specifically includes:

[0030] By using a protocol parsing gateway deployed on port equipment, energy-related register data from the programmable logic controller inside the heterogeneous equipment is collected.

[0031] The protocol parsing gateway will upload the register data, which follows different industrial communication protocols, to the data aggregation node through the industrial control network to form the raw energy consumption data stream. The raw energy consumption data stream includes the instantaneous power directly measured by the native controller of the heterogeneous device.

[0032] In this embodiment of the application, step S1 is the starting point and data foundation for solving the core technical problem of "data heterogeneity" in the energy consumption analysis of all port equipment. The core technical action of this step is to realize the unified access and aggregation of the dispersed and heterogeneous underlying energy consumption data of port equipment, so as to provide the raw data input with consistent format and reliable source for all subsequent analysis steps.

[0033] In the embodiments of this application, heterogeneous equipment refers to large-scale operating machinery that exists at the port operation site, is produced by different manufacturers, and is equipped with incompatible control systems and communication interfaces. These devices typically include quay cranes for ship loading and unloading, yard cranes for yard handling, and container trucks for horizontal transportation. Due to differences in manufacturers and models, the industrial communication protocols followed by their internal controllers in collecting and outputting energy consumption data are different.

[0034] In this embodiment of the application, the industrial control network refers to a dedicated communication network that connects port field equipment with the central monitoring system. This network is used to transmit equipment control commands and operating status data. Different types of equipment may be connected to different subnets or adopt different physical layer and link layer standards, but they can all ultimately send the data to the designated data aggregation point.

[0035] In this embodiment of the application, the raw energy consumption data stream refers to a real-time data sequence that is directly read from the device's underlying controller without any business logic processing. The core data content of this data stream is the instantaneous power value, which is directly derived from the physical measurement of the current power consumption by the device's power monitoring module.

[0036] In this embodiment of the application, the implementation of step S1 relies on a key hardware device, the protocol parsing gateway, deployed at the site of each port equipment. This gateway is an industrial-grade embedded computing device that is physically connected to the programmable logic controller inside the equipment. The programmable logic controller is the core control unit of the equipment. It is connected to sensors that measure current and voltage through an internal bus or input / output module, and stores the calculated instantaneous power value in a specific register. The first technical action of the protocol parsing gateway is to periodically read power data from these designated registers according to the preset configuration.

[0037] In this embodiment, the core function of the protocol parsing gateway is to resolve differences in communication protocols. When the gateway reads data from programmable logic controllers from different manufacturers, it faces a variety of industrial communication protocols, such as ModbusTCP, Profinet, or CANopen. The gateway integrates driver libraries for these protocols. Its second technical action is to perform protocol parsing and conversion. That is, regardless of the underlying protocol, the gateway parses the binary power value in the register into a processable value and encapsulates it into a unified data format that can be transmitted on industrial control networks (e.g., using a unified JSON format and marking the source).

[0038] In this embodiment, after completing the local protocol conversion, the protocol parsing gateway performs the third technical action: continuously uploading the encapsulated data packets to a central data aggregation node through the industrial control network accessed by the device. This data aggregation node can be a server or a server cluster, responsible for receiving data from all gateways across the field, including power data packets from hundreds or thousands of heterogeneous devices that may generate multiple times per second. These data packets are aggregated to form a continuous, high-concurrency raw energy consumption data stream. The instantaneous power values ​​in this data stream have the highest fidelity because they are directly derived from the real-time measurement of the device's native controller, avoiding errors or tampering that may be introduced by intermediate links, and ensuring the accuracy of all subsequent analyses.

[0039] In this embodiment, the direct technical effect of step S1 is to build a unified and reliable data input pipeline for the entire energy consumption analysis process. Through a combination of hardware (protocol parsing gateway) and software (protocol driver), it effectively overcomes the obstacle of data access failure caused by different device brands, models, and protocols. This step does not perform business interpretation or status judgment on the data, but focuses on ensuring that the most original physical signals that characterize the instantaneous energy consumption status of the device can be completely, accurately, and consistently collected and centralized. This is the first step in solving the data heterogeneity problem. It makes it possible to perform standardized time-series analysis, status identification, and energy efficiency modeling on all devices. Without the underlying data unification achieved by this step, any advanced analysis would be impossible due to the chaos and lack of data sources.

[0040] S2. Perform protocol parsing and time alignment processing on the original energy consumption data stream to generate energy consumption time-series data with a unified timestamp and standardized device identifier.

[0041] In some embodiments, the original energy consumption data stream is subjected to protocol parsing and time alignment processing to generate energy consumption time-series data with a unified timestamp and standardized device identifier, specifically including:

[0042] Based on the industrial communication protocols followed by the heterogeneous devices, the original energy consumption data stream is parsed to extract the energy consumption measurement value and the device's native identifier.

[0043] The energy consumption measurement values ​​are converted into a unified measurement unit, and a unified time reference stamp is applied to the converted data;

[0044] Based on a predefined device coding mapping relationship, the native device identifier is converted into a globally unique standardized device identifier.

[0045] In this embodiment, step S2 is a key transformation step that takes over the original data stream obtained in S1 and solves the core problem of data heterogeneity. The core technical action of this step is to transform the aggregated, mixed and time-asynchronous original data packets into a standardized time-series data set that can be directly and accurately analyzed by subsequent algorithms through deep parsing, time synchronization and unified identification processing.

[0046] In this embodiment, step S2 performs the first processing step, deep protocol parsing, on the raw energy consumption data stream at the data aggregation node. Although the protocol parsing gateway performs preliminary protocol conversion in S1, each data packet in the data stream received by the data aggregation node still contains metadata that identifies its source and meaning. The parsing here is for the application layer structure of the data packet. Specifically, the data aggregation node runs a set of protocol parsing services. These services call the corresponding parsing rules according to the protocol type (such as Modbus TCP header) identified in the data packet. The technical action of parsing is to accurately extract two key elements from the data packet. One is the energy consumption metering value, which represents the instantaneous power value. The other is the device native identifier, which is the information encapsulated by the gateway in S1 to identify the source of the original device, such as the device's IP address or its station number in a specific controller. For example, the parsing service will parse "instantaneous power is 150.3" and "this data comes from the device with IP address 192.168.10.5" from a data packet.

[0047] In this embodiment of the application, after data extraction is completed, step S2 immediately performs the second key technical action, namely time alignment and unit unification. First, for the extracted energy consumption measurement value, the system will forcibly convert it into a unified measurement unit, such as unifying the power unit to "kilowatt". This conversion is based on the dimensional configuration of various device sensors pre-stored in the system. More importantly, a unified time reference stamp is applied. Since there are slight deviations in the local clocks of each device and gateway, directly using their attached timestamps will cause the full-time data to be unable to be aligned on a unified time axis. The implementation method of this step is to use the high-precision clock synchronized by the data aggregation node itself through the network time protocol as a reference. When the data packet arrives, the node will record the current accurate UTC time and use this timestamp to overwrite or associate the original device local timestamp in the data packet, thereby ensuring that all data points in the field have an absolutely synchronized time reference.

[0048] In this embodiment, the third key technical action of step S2 is the standardized mapping of device identifiers. The "native device identifier" (such as IP address) extracted from the data packet is effective for network communication, but it lacks intuitiveness and consistency for port business management. This step converts the "native device identifier" into a "standardized device identifier" based on a predefined "device code mapping relationship table" stored in the database. This mapping relationship table is configured by the administrator during system initialization. It establishes a fixed association such as "IP address 192.168.10.5" corresponding to "yard bridge number RTG-08". After parsing the native identifier, the system queries this mapping table in real time to complete the identifier conversion, thereby assigning a globally unique and business-meaning device identifier to each row of data.

[0049] In this embodiment, after the above three consecutive technical actions, the system finally outputs "energy consumption time series data," which is a well-structured data set. Each record contains three core fields: a unified timestamp assigned by the clock of the data aggregation node, a standardized device identifier obtained from the mapping table, and an instantaneous power value converted to a unified unit. This time series data is the direct input for all subsequent state identification and energy consumption analysis algorithms to run correctly. It fundamentally eliminates analysis errors caused by protocol differences, clock asynchrony, and inconsistent identifiers, laying the data foundation for building a consistent energy consumption view across the entire field. The technical effect of this step is to thoroughly complete the cleaning and standardization process from the raw signal to the analytically usable data. It is an essential technical link to solve data heterogeneity and achieve accurate time series analysis.

[0050] S3. Input the energy consumption time series data into a predefined port equipment energy consumption state machine, and divide the energy consumption time series data into energy consumption state sequences corresponding to different physical states by matching power characteristic rules, wherein the physical states include at least the operating state, the idle state, and the standby state.

[0051] In some embodiments, the energy consumption time-series data is input into a predefined port equipment energy consumption state machine, and the energy consumption time-series data is divided into energy consumption state sequences corresponding to different physical states by matching power characteristic rules, specifically including:

[0052] The instantaneous power in the energy consumption time series data is input into the energy consumption state machine of the port equipment;

[0053] The port equipment energy consumption state machine continuously analyzes the input data according to the power characteristic judgment rules preset for different types of equipment such as quay cranes, yard cranes and container trucks. The power characteristic judgment rules include at least the power threshold rules for distinguishing whether the equipment is in an effective working phase, the power change trend rules for identifying equipment start-up and shutdown and load changes, and the duration rules for confirming state stability.

[0054] Based on the continuous analysis, the physical operating state corresponding to each time point in the energy consumption time series data is labeled, thereby outputting the energy consumption state sequence.

[0055] In the embodiments of this application, step S3 is the creative core link to solve the core technical problem of state ambiguity in port energy consumption analysis. The core technical action of this step is to design and apply an intelligent software logic model, namely a state machine. This model can imitate the judgment logic of human experts and automatically analyze and label the continuous curve generated by S2, which only contains instantaneous power values, as discrete state segments with clear physical meaning.

[0056] In this embodiment of the application, the port equipment energy consumption state machine mentioned in step S3 is a software logic model running in a server. The state machine has a variety of preset judgment logics. It receives continuous time-series data input and outputs a corresponding equipment physical state label based on the current data point and its historical context.

[0057] In this embodiment of the application, the implementation of step S3 begins by continuously feeding the energy consumption time series data generated in S2 into the port equipment energy consumption state machine as an input stream. For each data point that arrives, the state machine identifies the equipment type based on its standardized equipment identifier (such as RTG-08) and calls the power characteristic judgment rule set specifically preset for that type of equipment for analysis.

[0058] In this embodiment, the power characteristic judgment rule is a set of composite judgment conditions designed to achieve accurate state identification. Its specific implementation includes three types of cooperating sub-rules:

[0059] The first type is the power threshold rule, which is the basic method for initial screening of the status. For example, for quay crane equipment, the rules preset multiple power ranges; if the instantaneous power value is continuously in a higher range, it is initially judged that it may be in a loaded operation state; if the power value falls in a lower range, it may correspond to an unloaded movement state.

[0060] The second category is power change trend rules, which is a key means of identifying the instantaneous state transition. Specifically, the state machine maintains historical power data within a sliding time window in memory and calculates the first-order difference of instantaneous power in real time to characterize the change trend. For example, when the power value is detected to rise rapidly and continuously in a short period of time, and the first-order difference is continuously greater than a set positive gradient threshold, even if the absolute power has not yet reached the operating threshold, the state machine can identify it as a "start-up" or "load increase" trend, as an early judgment basis for the state transition.

[0061] The third category is the duration rule, which is a necessary means to ensure the stability and anti-interference of state determination. This rule requires that any state that is initially determined must be stable for a minimum period of time before it can be finally confirmed and output. For example, a power spike caused by accidental fluctuations, even if it exceeds the operation threshold, will be filtered out by this rule because the duration is too short, thereby avoiding the generation of a large number of meaningless fragmented state segments.

[0062] In this embodiment, the state machine comprehensively applies the above rules to continuously analyze the data stream. Internally, it implements a state transition logic and maintains the current state of the device. For each newly input power data point, it first applies the power threshold rule to determine a "candidate state". Then, it checks the power change trend to determine whether the device load is in the rising, falling, or stable phase. This trend information is used to help verify the rationality of the state transition. Then, it checks whether the duration of the current "candidate state" meets the minimum duration required by the state. Only when the threshold condition, the trend, and the duration all meet the requirements will the state machine finally confirm that the time point belongs to a certain physical operating state and label the time point with labels such as "operating state", "idle state", or "standby state". This process is performed sequentially for each time point in the data stream.

[0063] In this embodiment, after continuous scanning and analysis of all time-series data, the state machine finally outputs an energy consumption state sequence. This sequence is a new data structure that not only contains the original timestamp and device identifier, but more importantly, it associates each time point with a physical operating state determined by the state machine. The technical effect of this step is to revolutionize the transformation of the ambiguous "power value" sequence into a clear "behavioral state" sequence. Through a set of configurable and automated rule algorithms that mimic expert experience, it solves the problem in the background technology of not being able to directly interpret the essence of equipment operation from energy consumption data. It provides a crucial state foundation for subsequently associating energy consumption with specific production operation tasks (step S4), and is the core technical support for the entire method to achieve the leap from physical signals to business knowledge.

[0064] In some embodiments, the power characteristic judgment rules preset for different types of equipment such as quay cranes, yard cranes, and container trucks specifically include:

[0065] The rules preset for the quay crane equipment are configured with different power threshold ranges for identifying unloaded lifting of spreaders, lifting with boxes, and unloaded movement of the trolley.

[0066] The rules preset for the yard crane equipment include power change rate characteristics used to distinguish between the travel of the main trolley, the travel of the auxiliary trolley, and the operation of the hoisting mechanism;

[0067] The rules preset for container truck equipment include a power benchmark value and fluctuation range based on the idle fuel consumption and driving load model.

[0068] In this embodiment of the application, this part is a detailed expansion and deepening of the core creative component in step S3—the power characteristic judgment rule—aiming to clarify how to tailor differentiated state recognition logic for the different physical structures and operational characteristics of the main heterogeneous equipment in the port, thereby accurately solving the problem of state ambiguity.

[0069] In this embodiment, the power characteristic judgment rule preset for quay crane equipment is implemented based on static matching of multi-level power threshold intervals. The operation characteristics of quay cranes are that the power level is strongly correlated with the load of the spreader (unloaded or with a container) and the mechanism action (lifting or trolley movement). Therefore, the specific implementation of this rule is to preset multiple non-overlapping power threshold intervals for equipment such as quay cranes in the state machine. When the state machine is running, it compares the real-time input instantaneous power value with these preset intervals. For example, a low and stable power interval may be mapped to the "trolley moving without load" state, a medium power interval may correspond to "spreader lifting without load", and a continuous high power interval is judged as a typical heavy-load operation state such as "lifting with a container". This rule design is directly derived from the physical characteristics of quay cranes. Its technical effect is that it can effectively distinguish between effective operation (with a container) and ineffective but energy-consuming unloaded action, providing a key judgment basis for evaluating operation efficiency and unloaded energy consumption.

[0070] In this embodiment, the core of the power characteristic judgment rule preset for the yard crane equipment is to dynamically monitor the power change rate characteristics. The operation characteristics of the yard crane are that its multiple mechanisms, such as the trolley, hoist, etc., may move independently or in combination. A single absolute power value is difficult to distinguish the specific actions. Therefore, the specific implementation of this rule is that the state machine calculates the first difference of the instantaneous power in real time, that is, the power change rate, and monitors its change pattern. For example, when the trolley starts to travel, the power will have a specific amplitude of jump and then remain relatively stable. The power change characteristics of the trolley are different, while the hoisting mechanism is accompanied by a more dramatic power change curve. The rule presets the power change rate characteristic patterns of these typical actions (such as the rise slope and steady-state fluctuation range). By matching the real-time calculated change rate with the preset pattern, the technical effect of this rule is that it can accurately distinguish the specific mechanical actions being performed by the yard crane, and achieve more refined state identification than simple threshold judgment. This is crucial for analyzing the energy consumption composition of complex composite actions.

[0071] In this embodiment, the core of the power characteristic judgment rule preset for container truck equipment is to establish a benchmark-fluctuation model based on the characteristics of the power system. As a mobile device driven by an internal combustion engine, the energy consumption characteristics of container trucks are fundamentally different from those of fixed electric drive equipment. The specific implementation of this rule is as follows: First, a "idle fuel consumption and driving load model" is preset according to the container truck model. This model defines the typical power benchmark value at idle speed and the power fluctuation range under different driving loads (such as flat roads, slopes, and heavy loads). When the state machine is running, it not only looks at the absolute value of instantaneous power, but also pays attention to its offset and fluctuation characteristics relative to the idle benchmark. For example, a slight fluctuation in power near the idle benchmark may correspond to "stopping and waiting", while a power that is significantly and continuously higher than the benchmark value corresponds to "driving state". The load is further judged by combining the fluctuation range. The technical effect of this rule design is to overcome the identification difficulties caused by the instability of the power baseline of internal combustion engine equipment, so that the state machine can adapt to the dynamic and continuously changing working conditions of container trucks and accurately distinguish its standby, empty driving and loaded driving states, filling the technical gap in the accurate identification of the energy consumption state of mobile equipment.

[0072] In some embodiments, the power change trend rule is applied in the following ways:

[0073] In the energy consumption time series data, the first-order difference of instantaneous power is calculated using a sliding time window;

[0074] When the absolute value of the first-order difference continuously exceeds the set positive gradient threshold, it is identified as a load increase trend, which serves as an auxiliary condition for judging the transition from the state to the working state.

[0075] When the absolute value of the first-order difference is continuously lower than the set negative gradient threshold, it is identified as a load decline trend, which serves as an auxiliary condition for judging whether the state is transitioning to an idle or standby state.

[0076] In this embodiment, this part is a further refinement of the implementation method of the key sub-rule "power change trend rule" in step S3. Its creativity lies in the introduction of dynamic calculus calculation, which gives the state machine the ability to sense the rate of energy change, thereby realizing the instantaneous and sensitive capture and prediction of the device state transition. This is an advanced technical means to solve the problem of state ambiguity.

[0077] In this embodiment, the sliding time window is the basic data container for trend calculation. The state machine maintains a first-in-first-out data queue in memory. This queue continuously loads the latest instantaneous power values ​​with a uniform timestamp and discards old data that exceeds the window duration limit. For example, for a sliding time window set to 5 seconds, it always contains all power sampling points within the last 5 seconds. This implementation ensures that the data on which the trend analysis depends is a direct reflection of the device's most recent operating status and avoids interference from outdated historical data on the current trend judgment.

[0078] In this embodiment, the first-order difference of instantaneous power is the core algorithm operation for quantifying the trend of power change. This calculation is performed continuously within a sliding window. The first-order difference is mathematically defined as the difference between two adjacent data points. Its specific calculation formula is expressed as the instantaneous power value at the current moment minus the instantaneous power value at the previous sampling moment. The sign and magnitude of this difference value directly quantify the direction and rate of change of power per unit time. For example, a large positive difference value means that the power of the equipment is increasing rapidly, which may correspond to the start-up of the mechanism or a sudden increase in load.

[0079] In this embodiment, the set positive gradient threshold and the set negative gradient threshold are key criteria for converting continuous changes into discrete trend judgments. These two thresholds are pre-configured based on the physical characteristics of different types of equipment under typical operating conditions. The positive gradient threshold is set to a significant positive number, which represents the minimum power increase rate required to be considered as "effective start-up" or "significant load increase". The negative gradient threshold is set to a significant negative number, the absolute value of which represents the minimum power decrease rate required to be considered as "effective stop-up" or "significant load decrease". For example, for a large quay crane, the power increases very quickly when its hoisting motor starts, so its positive gradient threshold may be set to a high value; while the power increases slowly when the throttle of a truck is gently pressed, and its corresponding positive gradient threshold is lower. The precise configuration of these two thresholds enables the system to filter out small power fluctuations caused by grid fluctuations or measurement noise, thereby focusing on identifying truly meaningful state transition events.

[0080] In this embodiment, the complete application of the trend rule is a continuous monitoring and judgment process. After calculating a series of first-order difference values ​​at consecutive time points, the state machine compares them with a set threshold. When the system detects that several consecutive (e.g., three) first-order difference values ​​are greater than the positive gradient threshold, it determines that a "load increase trend" has occurred. This trend signal itself does not directly define the final state, but serves as a high-priority auxiliary condition. For example, if the state machine determines that the equipment is in an "idle state" but simultaneously detects a "load increase trend," it will predict in advance that the equipment may be transitioning to an "operating state," thereby paying more attention to whether the subsequent power reaches the operating threshold or adjusting the judgment logic for state confirmation. Conversely, the identification of a "load decrease trend" serves as an early warning of transition to a low-energy-consumption state. The core effect of this technique is to give the state machine "foresight," enabling it to no longer make passive judgments based solely on the absolute value of the current power, but to perceive the intention of state transition in advance based on the direction of power change. This greatly improves the timeliness and accuracy of state division, especially for port equipment with frequent start-stop cycles and rapidly changing operating conditions. This is an indispensable technical link for achieving accurate state segmentation.

[0081] S4. Based on the port operation plan and equipment operation log, label the operation status in the energy consumption status sequence with operation logic tags to obtain the labeled status sequence.

[0082] In some embodiments, based on the port operation plan and equipment operation log, operation logic labels are marked for the operation states in the energy consumption state sequence to obtain the labeled state sequence, specifically including:

[0083] Extract the operation instructions with time information from the port operation plan and equipment operation log;

[0084] The time information of the work instruction is compared with the time interval of the work status;

[0085] When the time information of the work instruction falls into or overlaps with the time interval of the work state, the process information represented by the work instruction is marked as the work logic label of the corresponding work state segment.

[0086] After labeling, the energy consumption state sequence is transformed into a labeled state sequence with operation logic tags, which are specifically associated with at least one process step in container loading and unloading, horizontal transportation, or yard sorting.

[0087] In this embodiment, step S4 is a key data fusion step that solves the core technical problem of "business isolation" in port energy consumption analysis. The core technical action of this step is to perform spatiotemporal correlation and semantic fusion between the energy consumption state sequence generated in S3, which only reflects the physical operation behavior of the equipment, and the business plan information from the port production management system, thereby giving the abstract energy consumption data a specific production task connotation.

[0088] In this embodiment, step S4 first requires obtaining and processing two types of key data sources from external business systems. The first type of data source is the port operation plan, which usually comes from the port production operating system. It plans tasks such as "quay crane A10 will perform loading operations on 'ocean-going vessels' from 14:00 to 16:00" in the form of structured instructions. The second type of data source is the equipment operation log, which is automatically recorded by the equipment control system and contains more granular actual operation events and timestamps, such as "truck YT25 will perform transportation from bay B02 to quay crane A10 from 14:05 to 14:15". The specific implementation of step S4 is to run a data interface service, which periodically queries and extracts operation instruction records with clear start and end time information from these external system databases.

[0089] In this embodiment of the application, after obtaining the external work instruction, step S4 performs the core data association operation, namely time interval comparison. The technical implementation of this operation is accomplished through a time matching algorithm. The algorithm takes all time intervals marked as "work status" in the energy consumption status sequence output by S3 as segments to be labeled. For each such work status time interval, the algorithm will traverse all work instructions extracted from the outside and check whether there is such an instruction whose planned time interval intersects with the current work status time interval on the time axis, i.e., overlaps, or the work status time interval is completely contained within the time interval of a certain work instruction. This "falling into or overlapping" judgment logic, rather than strict equality, is to adapt to the reasonable deviation between the actual operating time of the equipment and the planned time in actual production.

[0090] In this embodiment of the application, after the time matching algorithm successfully finds one or more associated work instructions, step S4 performs semantic annotation. The system extracts the process information represented by these associated work instructions. This information is specific production business semantics, such as "performing loading operations for 'ocean-going vessels'", "transferring empty containers from the yard to the maintenance area", or "organizing containers in the yard". Subsequently, the system writes this process information as a "work logic tag" into the corresponding "work status" data record. If a certain work status time period cannot be matched with any work instruction, the status will retain its physical status tag but will not be attached with a work logic tag.

[0091] In this embodiment, after the above association and annotation processing, the original energy consumption state sequence is transformed into a "labeled state sequence". This new sequence is the product of deep integration of physical world energy consumption signals and production business information. Each record not only indicates when the equipment is in what physical operating state, but more importantly, it indicates which type of process the equipment is performing in that state, such as "container loading and unloading", "horizontal transportation" or "yard sorting". The direct technical effect of this step is to completely break down the information barrier between energy consumption data and production business. Through specific data interfaces, time matching algorithms and label mapping technology, it successfully associates the physical fact that "the equipment is consuming electricity" with the business fact that "the equipment is consuming electricity for which ship and for which loading and unloading task". This provides a fundamental technical solution to the core problem of not being able to trace energy consumption to specific production tasks in the background technology, and lays a decisive foundation for the subsequent construction of an energy flow model oriented towards business analysis.

[0092] S5. Based on the labeled state sequence, construct an energy flow model with a three-layer mapping of device-state-energy consumption.

[0093] In some embodiments, based on the labeled state sequence, a three-layer energy flow model mapping device-state-energy consumption is constructed, specifically including:

[0094] Based on the standardized device identifier, the data in the labeled state sequence are categorized by device;

[0095] For the classification data of each device, based on the status type and the operation logic label, it is divided into continuous and non-overlapping time intervals;

[0096] Integrate the power value within each time interval to obtain the total energy consumption value within that time interval.

[0097] A status energy consumption unit record is generated using the standardized equipment identifier, the status type, the operation logic label, the time interval and its corresponding total energy consumption value as elements.

[0098] The energy flow model is formed by summarizing the state energy consumption unit records of all devices in the field.

[0099] In this embodiment of the application, step S5 is a key step in building a structured, computable, and traceable core model of energy consumption data, based on the business semantic fusion achieved in step S4. The core technical action of this step is to transform the sequence data with time, equipment, status, and operation tags into a full-field energy flow knowledge graph with "state energy consumption unit" as the atomic record through classification, segmentation, integral calculation and structured encapsulation, thereby providing a directly computable underlying data model for the final statistical analysis product.

[0100] In this embodiment of the application, the implementation of step S5 begins with the first data organization of the labeled state sequence output by S4, that is, the classification of equipment dimensions according to the standardized equipment identifier. The system traverses the entire labeled state sequence and distributes all data records to virtual containers with each equipment as an independent unit according to the equipment identifier in each record, such as RTG-08 or QC001. The essence of this specific technical means is to perform the first dimensional division of the entire field data. Its effect is to build the basic layer of "equipment" in the energy flow model, ensuring that all subsequent calculations and analyses can be carried out with a single equipment as the basic traceability unit.

[0101] In this embodiment, after the equipment is classified, step S5 performs the second key technical operation on the independent data container of each equipment, namely, finely dividing the time segment according to the status type and operation logic label. The system will scan all status records of a single equipment in chronological order. The specific implementation logic is that when the "status type" (such as operation, no load) or "operation logic label" (such as "loading ship A" or "disposal of containers in the yard") in the continuous record changes, the system sets a dividing point at this point. Finally, the continuous operation time axis of the equipment from morning to night is cut into a series of continuous and non-overlapping time intervals, in which the status type and operation logic label within each interval remain completely consistent. For example, equipment RTG-08 may be divided into intervals such as "[09:00-09:05, no load, no operation label]" and "[09:05-09:15, operation, loading ship A]". This technical action is the basis for building the "status" and "operation task" layer. It ensures that the behavioral semantics of the equipment within each basic computing unit are pure and single.

[0102] In this embodiment, for each defined, semantically pure time interval, step S5 performs its core quantization calculation, which is to integrate the instantaneous power value within the interval to obtain the total energy consumption value. The calculation logic is that the system extracts the instantaneous power values ​​of all sampling points of the device within this time period from the high-precision stored original energy consumption time series database generated in step S2, based on the start and end times of the time interval. The formula for calculating the total energy consumption value is physically expressed as the integral of the instantaneous power over time. The specific implementation of this calculation process in the system is usually completed using numerical integration methods, such as discretizing continuous time into multiple small time intervals, calculating the energy consumption (power multiplied by time) in each small time interval, and then summing them up. This calculation process ultimately quantifies the device's "power performance" into a "energy consumption" value with clear physical meaning.

[0103] In this embodiment of the application, after completing the above integral calculation, step S5 encapsulates all these discrete information elements into a complete "state energy consumption unit record". Each record is a structured data object, which must contain at least the following core elements, namely, standardized equipment identifier, state type, operation logic label, start and end time of time interval, and calculated total energy consumption value. For example, a record may be "{equipment: QC001, state: operation, operation label: unloading operation ('Xingchen' vessel), time interval: 10:00-10:02, total energy consumption: 5.0kWh}". This encapsulation action is a key step in transforming the original data into a knowledge unit. It creates a self-contained and self-explanatory minimum energy consumption fact unit.

[0104] In this embodiment, the final technical action of step S5 is to summarize all state energy consumption unit records generated by all devices in the field, forming a "three-layer mapping energy flow model of device-state-energy consumption". This model can be a database table or a set of data structures in memory. The "three-layer mapping" characteristic of this model is specifically reflected in its data structure, which naturally supports drilling down from any layer. For example, one can view the energy consumption of a certain device (device layer) under all operating states (state layer) and further view the energy consumption details of a specific unloading task (operating task layer). The final technical effect of this step is to construct an analytically oriented, strongly structured energy consumption data cube. It completely solves the problem of fragmented energy consumption data and the inability to systematically associate it with device status and business tasks in the background technology. It provides a unique, accurate, and efficient data foundation for daily aggregation and drill-down traceability analysis of arbitrary dimensions and granularity in step S6, and is the core technology for realizing transparent and refined management of energy consumption throughout the field.

[0105] S6. Based on all state energy consumption units in the energy flow model, aggregate and calculate according to the natural day time window to generate the port's daily total energy consumption data product.

[0106] In some embodiments, based on all state energy consumption units in the energy flow model, aggregate calculations are performed according to a natural day time window to generate a port daily total energy consumption data product, specifically including:

[0107] Using natural days as the statistical period, the state energy consumption unit records of all time interval segments in the energy flow model within the statistical period are traversed and filtered.

[0108] The selected status energy consumption unit records are classified according to at least one of the following dimensions: their standardized equipment identifier, status type, and operation logic label.

[0109] The total energy consumption values ​​in the state energy consumption unit records under the same category are accumulated and summed to generate a port daily total energy consumption data product. The port daily total energy consumption data product includes at least the energy consumption summary results at each level obtained by accumulating and calculating based on different dimensions, and saves the association relationship with the underlying state energy consumption unit records to support drill-down traceability analysis from any summary result to specific equipment, status and operation tasks.

[0110] In this embodiment, step S6 is the final output of the method process. Based on the precise energy flow model constructed in the aforementioned steps, it addresses the core requirements of global insight and root cause tracing in port energy consumption management. The core technical action of this step is to perform multi-dimensional and reversible aggregation calculations on the full-field energy flow model generated in S5, generating standardized data products that not only present macroscopic results but also support microscopic drill-down, thereby transforming the complex underlying data processing into intuitive and actionable information for management decision-making.

[0111] In this embodiment, step S6 begins by defining a specific time statistics window and traversing and filtering the energy flow model on a daily basis. Specifically, the system executes a query on the energy flow model database built in S5 based on the date of user query or timed task trigger (e.g., October 27, 2023). The core condition of this query is to filter out all records in the "state energy consumption unit records" whose time interval (the time window defined by the start time and end time) intersects with the natural day. This means that as long as a record's energy consumption behavior occurs within that day, whether it starts on the previous day and ends on the current day, or starts on the current day and ends on the next day, its energy consumption within the time window of that day will be accurately included in the statistics. This filtering mechanism ensures the completeness and completeness of the daily statistics.

[0112] In this embodiment of the application, after obtaining all relevant state energy consumption unit records for the target day, step S6 performs a multi-dimensional data classification operation. The system groups the selected record set according to preset or user-specified analysis dimensions. These dimensions are directly derived from the core elements of the state energy consumption unit records, mainly including standardized equipment identifiers, state types, and operation logic tags. For example, the system can classify by "equipment type" (all quay cranes), or by "state type" (all empty states), or by "operation logic tag" (all 'loading' operations). It also supports combined classification of these dimensions (such as "energy consumption of quay cranes in loading operation state"). The technical significance of this classification operation is that it slices the energy consumption data according to the inherent "equipment-state-operation" three-layer structure of the energy flow model, thus preparing the data for subsequent multi-angle analysis.

[0113] In this embodiment of the application, after the data classification is completed, step S6 performs a key cumulative summation calculation on the records in each classification group. For all state energy consumption unit records classified into the same group, the system arithmetically sums the total energy consumption values ​​obtained by integrating them. In specific implementation, this calculation process uses the SUM aggregation function on the database query results. For example, for the classification of "total energy consumption of all trucks on October 27", the system will add up the total energy consumption values ​​of all records where the equipment is identified as a truck and the time falls on that day. This cumulative calculation aggregates the energy consumption scattered in thousands of time segments into a macro indicator with clear business meaning.

[0114] In this embodiment, the results of the above-mentioned aggregation calculation are systematically organized and output as a "port daily total energy consumption data product." This data product is not a single number, but a structured report or data view. It at least includes the energy consumption summary results at each level obtained from the above-mentioned different dimension classification calculations, such as "daily total power consumption of the entire port," "daily total power consumption of quay cranes," "daily total power consumption of operational status," and "daily total power consumption of loading and unloading vessel A." The technical feature of this step is that, while generating these summary data, the system must persistently save the correlation between each summary result and the underlying energy consumption unit records on which it is based. The preservation of this correlation enables the data product to have the ability to "drill down and trace." When the manager discovers "daily quay crane empty energy consumption," the system can perform a "drill-down trace" operation. When energy consumption is abnormally high, this correlation allows for direct drill-down to identify which specific quay cranes (such as QC001 and QC003) caused the anomaly. Further investigation reveals the specific time periods during which these cranes were idle and the reasons for their ineffective utilization (such as waiting for instructions). The ultimate technical benefit of this step is the realization of the entire methodology's technological value. It not only generates the statistical reports required by high-level management but, more importantly, ensures the traceability of data products and underlying facts. This fundamentally solves the shortcomings of background technologies in energy consumption analysis, which often "see the forest but not the trees" and fail to pinpoint the root causes of specific problems. It provides technical support for ports to achieve refined management, from macro-statistics to micro-optimization, and from results presentation to root cause governance.

[0115] like Figure 2 The diagram shown is a functional block diagram of a port equipment daily total energy consumption analysis system provided in an embodiment of this application.

[0116] The daily total energy consumption analysis system 100 for all port equipment described in this application can be installed in an electronic device. Depending on the functions implemented, the daily total energy consumption analysis system 100 for all port equipment may include a data acquisition and aggregation module 101, a data preprocessing and standardization module 102, an energy consumption status identification module 103, an operation logic fusion module 104, an energy flow modeling module 105, and a multi-dimensional aggregation and traceability analysis module 106. The modules described in this application can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0117] In this embodiment, the functions of each module / unit are as follows:

[0118] The data acquisition and aggregation module 101 is used to acquire raw energy consumption data streams uploaded by various heterogeneous devices throughout the port through their respective industrial control networks;

[0119] The data preprocessing and standardization module 102 is used to perform protocol parsing and time alignment processing on the original energy consumption data stream to generate energy consumption time-series data with a unified timestamp and standardized device identifier;

[0120] The energy consumption status identification module 103 is used to input the energy consumption time series data into a predefined port equipment energy consumption status machine, and divide the energy consumption time series data into energy consumption status sequences corresponding to different physical states by matching power feature rules, wherein the physical states include at least operating state, idle state and standby state.

[0121] The operation logic fusion module 104 is used to label the operation status in the energy consumption status sequence with operation logic tags based on the port operation plan and equipment operation log, so as to obtain the labeled status sequence.

[0122] The energy flow modeling module 105 is used to construct an energy flow model with a three-layer mapping of device-state-energy consumption based on the labeled state sequence.

[0123] The multi-dimensional aggregation and traceability analysis module 106 is used to perform aggregation calculations based on all state energy consumption units in the energy flow model according to the natural day time window, and generate port daily total energy consumption data products.

[0124] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0125] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0127] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application.

[0128] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A method for analyzing the daily total energy consumption of all equipment in a port, characterized in that, The method includes: Acquire raw energy consumption data streams uploaded by various heterogeneous devices across the port through their respective industrial control networks; The original energy consumption data stream is subjected to protocol parsing and time alignment processing to generate energy consumption time-series data with a unified timestamp and standardized device identifier; The energy consumption time series data is input into a predefined port equipment energy consumption state machine. By matching power characteristic rules, the energy consumption time series data is divided into energy consumption state sequences corresponding to different physical states, wherein the physical states include at least the operating state, the idle state, and the standby state. Based on the port operation plan and equipment operation log, operation logic labels are marked for the operation status in the energy consumption status sequence to obtain the labeled status sequence; Based on the labeled state sequence, a three-layer energy flow model mapping device-state-energy consumption is constructed, specifically including: classifying the data in the labeled state sequence by device according to the standardized device identifier; dividing the classified data of each device into continuous and non-overlapping time intervals according to the state type and the operation logic label; performing an integral operation on the power value in each time interval to obtain the total energy consumption value in that time interval; generating a state energy consumption unit record using the standardized device identifier, the state type, the operation logic label, the time interval and its corresponding total energy consumption value as elements; and summarizing the state energy consumption unit records of all devices in the field to form the energy flow model. Based on all state energy consumption units in the energy flow model, aggregate calculations are performed according to natural day time windows to generate port daily total energy consumption data products.

2. The method for analyzing the daily total energy consumption of all equipment in a port as described in claim 1, characterized in that, Acquire raw energy consumption data streams uploaded by various heterogeneous devices across the port through their respective industrial control networks, specifically including: By using a protocol parsing gateway deployed on port equipment, energy-related register data from the programmable logic controller inside the heterogeneous equipment is collected. The protocol parsing gateway will upload the register data, which follows different industrial communication protocols, to the data aggregation node through the industrial control network to form the raw energy consumption data stream. The raw energy consumption data stream includes the instantaneous power directly measured by the native controller of the heterogeneous device.

3. The method for analyzing the daily total energy consumption of all equipment in a port as described in claim 1, characterized in that, The raw energy consumption data stream is subjected to protocol parsing and time alignment processing to generate energy consumption time-series data with a unified timestamp and standardized device identifier, specifically including: Based on the industrial communication protocols followed by the heterogeneous devices, the original energy consumption data stream is parsed to extract the energy consumption measurement value and the device's native identifier. The energy consumption measurement values ​​are converted into a unified measurement unit, and a unified time reference stamp is applied to the converted data; Based on a predefined device coding mapping relationship, the native device identifier is converted into a globally unique standardized device identifier.

4. The method for analyzing the daily total energy consumption of all equipment in a port as described in claim 1, characterized in that, The energy consumption time-series data is input into a predefined port equipment energy consumption state machine. By matching power characteristic rules, the energy consumption time-series data is divided into energy consumption state sequences corresponding to different physical states, specifically including: The instantaneous power in the energy consumption time series data is input into the energy consumption state machine of the port equipment; The port equipment energy consumption state machine continuously analyzes the input data according to the power characteristic judgment rules preset for different types of equipment such as quay cranes, yard cranes and container trucks. The power characteristic judgment rules include at least the power threshold rules for distinguishing whether the equipment is in an effective working phase, the power change trend rules for identifying equipment start-up and shutdown and load changes, and the duration rules for confirming state stability. Based on the continuous analysis, the physical operating state corresponding to each time point in the energy consumption time series data is labeled, thereby outputting the energy consumption state sequence.

5. The method for analyzing the daily total energy consumption of all equipment in a port as described in claim 4, characterized in that, The power characteristic judgment rules preset for different types of equipment such as quay cranes, yard cranes, and container trucks specifically include: The rules preset for the quay crane equipment are configured with different power threshold ranges for identifying unloaded lifting of spreaders, lifting with containers, and unloaded movement of the trolley. The rules preset for the yard crane equipment include power change rate characteristics used to distinguish between the travel of the main trolley, the travel of the auxiliary trolley, and the operation of the hoisting mechanism; The rules preset for container truck equipment include a power benchmark value and fluctuation range based on the idle fuel consumption and driving load model.

6. The method for analyzing the daily total energy consumption of all equipment in a port as described in claim 4, characterized in that, The power change trend rule is applied in the following ways: In the energy consumption time series data, the first-order difference of instantaneous power is calculated using a sliding time window; When the absolute value of the first-order difference continuously exceeds the set positive gradient threshold, it is identified as a load increase trend, which serves as an auxiliary condition for judging the transition from the state to the working state. When the absolute value of the first-order difference is continuously lower than the set negative gradient threshold, it is identified as a load decline trend, which serves as an auxiliary condition for judging whether the state is transitioning to an idle or standby state.

7. The method for analyzing the daily total energy consumption of all equipment in a port as described in claim 1, characterized in that, Based on the port operation plan and equipment operation logs, operation logic labels are marked for the operation states in the energy consumption state sequence to obtain the labeled state sequence, which specifically includes: Extract the operation instructions with time information from the port operation plan and equipment operation log; The time information of the work instruction is compared with the time interval of the work status; When the time information of the work instruction falls into or overlaps with the time interval of the work state, the process information represented by the work instruction is marked as the work logic label of the corresponding work state segment. After labeling, the energy consumption state sequence is transformed into a labeled state sequence with operation logic tags, which are specifically associated with at least one process step in container loading and unloading, horizontal transportation, or yard sorting.

8. The method for analyzing the daily total energy consumption of all equipment in a port as described in claim 1, characterized in that, Based on all state energy consumption units in the energy flow model, aggregate calculations are performed according to natural day time windows to generate a daily total energy consumption data product for the port, specifically including: Using natural days as the statistical period, the state energy consumption unit records of all time interval segments in the energy flow model within the statistical period are traversed and filtered. The selected status energy consumption unit records are classified according to at least one of the following dimensions: their standardized equipment identifier, status type, and operation logic label. The total energy consumption values ​​in the state energy consumption unit records under the same category are accumulated and summed to generate a port daily total energy consumption data product. The port daily total energy consumption data product includes at least the energy consumption summary results at each level obtained by accumulating and calculating based on different dimensions, and saves the association relationship with the underlying state energy consumption unit records to support drill-down traceability analysis from any summary result to specific equipment, status and operation tasks.

9. A daily total energy consumption analysis system for all port equipment, used to implement the daily total energy consumption analysis method for all port equipment as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition and aggregation module is used to acquire raw energy consumption data streams uploaded by various heterogeneous devices throughout the port through their respective industrial control networks; The data preprocessing and standardization module is used to perform protocol parsing and time alignment processing on the raw energy consumption data stream to generate energy consumption time-series data with a unified timestamp and standardized device identifier; The energy consumption status identification module is used to input the energy consumption time series data into a predefined port equipment energy consumption status machine, and divide the energy consumption time series data into energy consumption status sequences corresponding to different physical states by matching power feature rules, wherein the physical states include at least operating state, idle state and standby state. The operation logic fusion module is used to label the operation status in the energy consumption status sequence with operation logic tags based on the port operation plan and equipment operation log, so as to obtain the labeled status sequence. The energy flow modeling module is used to construct a three-layer energy flow model mapping device-state-energy consumption based on the labeled state sequence. Specifically, it includes: classifying the data in the labeled state sequence by device according to the standardized device identifier; dividing the classified data of each device into continuous and non-overlapping time intervals according to the state type and the operation logic label; performing an integral operation on the power value in each time interval to obtain the total energy consumption value in that time interval; generating a state energy consumption unit record using the standardized device identifier, the state type, the operation logic label, the time interval and its corresponding total energy consumption value as elements; and summarizing the state energy consumption unit records of all devices in the field to form the energy flow model. The multi-dimensional aggregation and traceability analysis module is used to aggregate and calculate the total daily energy consumption data of the port based on all state energy consumption units in the energy flow model according to the natural day time window.

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