On-site energy management method and device, computer device and storage medium

Abstract

The present disclosure relates to a field energy management method and device, computer equipment and storage medium. The field energy management method comprises: acquiring current production task, current energy consumption data and current energy consumption equipment data; determining whether the current equipment energy consumption is abnormal according to the current production task, the current energy consumption data and the current energy consumption equipment data. The present disclosure provides an energy consumption evaluation model based on edge side and combination of production and energy consumption, which can realize visual and accurate energy consumption evaluation.

Description

Technical Field

[0001] This disclosure relates to the field of communications, and in particular to a method and apparatus for on-site energy management, computer equipment, and storage medium. Background Technology

[0002] The "General Standard for Building Energy Conservation and Renewable Energy Utilization," released in 2021, explicitly stipulates that carbon emission calculation is a mandatory indicator in building construction. Furthermore, data from the "China Building Energy Consumption Research Report 2020" shows that in 2018, the building industry accounted for 51.2% of the total carbon emissions throughout its entire life cycle in China, with carbon emissions during the building operation phase accounting for 42.8% of the industry's total carbon emissions. Energy consumption is the primary factor contributing to carbon emissions. Therefore, the green, energy-efficient, and intelligent development of buildings is an inevitable trend. Summary of the Invention

[0003] The inventors discovered through research that while related intelligent building systems typically collect current energy consumption data for energy analysis, the energy analysis in these systems is disconnected from production planning, making data analysis more difficult for managers.

[0004] In view of at least one of the above technical problems, this disclosure provides a field energy management method and apparatus, computer equipment and storage medium, wherein the dedicated equipment and the main control equipment are in the same communication network, which can avoid data forwarding and facilitate functional expansion.

[0005] According to one aspect of this disclosure, an on-site energy management method is provided, comprising:

[0006] Obtain current production tasks, current energy consumption data, and current energy-consuming equipment data;

[0007] Based on the current production tasks, current energy consumption data, and current energy-consuming equipment data, determine whether the current equipment energy consumption is abnormal.

[0008] In some embodiments of this disclosure, determining whether the current equipment energy consumption is abnormal based on the current production task, current energy consumption data, and current energy-consuming equipment data includes:

[0009] Determine the standard energy consumption curve based on the current production tasks and current energy-consuming equipment data;

[0010] Determine the actual energy consumption curve based on current energy consumption data;

[0011] Determine the energy consumption deviation based on the actual energy consumption curve and the standard energy consumption curve;

[0012] Whether the energy consumption deviation is greater than the predetermined value determines whether the current energy consumption of the equipment is abnormal.

[0013] In some embodiments of this disclosure, determining whether the current equipment energy consumption is abnormal based on the current production task, current energy consumption data, and current energy-consuming equipment data includes:

[0014] Import the current production task, current energy consumption data, and current energy-consuming equipment data into a pre-trained equipment energy consumption assessment model to determine whether the current equipment energy consumption is abnormal.

[0015] In some embodiments of this disclosure, the on-site energy management method further includes:

[0016] Develop an equipment energy consumption assessment model;

[0017] Train the equipment energy consumption assessment model.

[0018] In some embodiments of this disclosure, the construction of the device energy consumption assessment model includes:

[0019] Collect the working status and duration of equipment under different production plans, and establish a relationship function between production plans and equipment operating status;

[0020] Establish a function relating energy consumption and runtime under different operating states of equipment;

[0021] Based on the relationship between production tasks and the corresponding equipment and equipment operating status, an equipment energy consumption assessment model is established to determine the relationship between production tasks and equipment energy consumption.

[0022] In some embodiments of this disclosure, training the device energy consumption assessment model includes:

[0023] Import normal evaluation data for a predetermined time period to determine the degree of model matching;

[0024] If the energy consumption deviation is less than the predetermined value, then the model matches;

[0025] If the energy consumption deviation is not less than the predetermined value, the model is adjusted and then the normal evaluation data is imported for training.

[0026] After model matching, evaluation outlier data is imported for training.

[0027] In some embodiments of this disclosure, the on-site energy management method further includes:

[0028] Based on current energy consumption data and current energy-consuming equipment data, determine the current optimal operating state and energy-saving effect;

[0029] It displays the current optimal operating status and energy-saving effect to users.

[0030] In some embodiments of this disclosure, determining the current optimal operating state and energy-saving effect based on current energy consumption data and current energy-consuming equipment data includes:

[0031] Import current energy consumption data and current energy-consuming equipment data into a pre-trained energy consumption model to determine the current optimal operating state and energy-saving effect.

[0032] In some embodiments of this disclosure, the on-site energy management method further includes:

[0033] Establish energy consumption models for equipment under different operating conditions and states;

[0034] Cluster fault data and operating condition data, and select training data according to different energy consumption models;

[0035] The energy consumption model is trained using training data.

[0036] In some embodiments of this disclosure, the on-site energy management method further includes:

[0037] Determine the current optimization parameters based on the current optimal operating status and energy-saving effect;

[0038] Display the current tuning parameters to the user and ask whether to perform automatic tuning;

[0039] If the user selects automatic tuning, automatic tuning will be performed based on the current tuning parameters;

[0040] If the user does not select automatic tuning, the tuning will be performed based on the user's input.

[0041] In some embodiments of this disclosure, the on-site energy management method further includes:

[0042] Analyze at least one of the following data: current production tasks, current energy consumption data, and current energy-consuming equipment data, in at least one of the following dimensions: time dimension, region dimension, and equipment dimension.

[0043] The analysis results are presented to the user.

[0044] In some embodiments of this disclosure, the on-site energy management method further includes:

[0045] In the event of abnormal power consumption of the current equipment, an abnormal message will be sent to the on-site alarm device, triggering the on-site alarm device to issue an on-site alarm.

[0046] According to another aspect of this disclosure, a field energy management device is provided, comprising:

[0047] The visualization module is configured to retrieve the current production tasks;

[0048] The data acquisition module is configured to acquire current energy consumption data and current energy-consuming device data;

[0049] The anomaly diagnosis module is configured to determine whether the energy consumption of the current equipment is abnormal based on the current production task, current energy consumption data, and current energy-consuming equipment data.

[0050] In some embodiments of this disclosure, the anomaly diagnosis module is configured to determine a standard energy consumption curve based on the current production task and current energy-consuming equipment data; determine an actual energy consumption curve based on the current energy consumption data; determine the energy consumption deviation based on the actual energy consumption curve and the standard energy consumption curve; and determine whether the current equipment energy consumption is abnormal based on whether the energy consumption deviation is greater than a predetermined value.

[0051] In some embodiments of this disclosure, the anomaly diagnosis module is configured to construct an equipment energy consumption assessment model; train the equipment energy consumption assessment model; import the current production task, current energy consumption data, and current energy-consuming equipment data into the pre-trained equipment energy consumption assessment model to determine whether the current equipment energy consumption is abnormal.

[0052] In some embodiments of this disclosure, the anomaly diagnosis module is configured to, when constructing an equipment energy consumption assessment model, collect the working status and duration of equipment under different production plans, establish a relationship function between production plans and equipment operating status; establish a relationship function between energy consumption and operating duration of different equipment operating states; and establish an equipment energy consumption assessment model between production tasks and equipment energy consumption based on the relationship between the equipment corresponding to the production tasks and the equipment operating status.

[0053] In some embodiments of this disclosure, the anomaly diagnosis module is configured to, when training the device energy consumption assessment model, import normal assessment data for a predetermined time period to determine the model matching degree; if the energy consumption deviation is less than a predetermined value, the model matches; if the energy consumption deviation is not less than the predetermined value, the model is adjusted, and normal assessment data is imported for training again; after the model matches, abnormal assessment data is imported for training again.

[0054] In some embodiments of this disclosure, the on-site energy management device further includes:

[0055] The real-time optimization module is configured to determine the current optimal operating state and energy-saving effect based on the current energy consumption data and the current energy-consuming equipment data;

[0056] The visualization module is also configured to display the current optimal operating status and energy-saving effect to the user.

[0057] In some embodiments of this disclosure, the real-time optimization module is configured to establish energy consumption models of the equipment under different operating conditions and running states; cluster fault data and operating condition data, and filter training data according to different energy consumption models; train the energy consumption model using the training data; import current energy consumption data and current energy-consuming equipment data into the pre-trained energy consumption model to determine the current optimal operating state and energy-saving effect.

[0058] In some embodiments of this disclosure, the real-time optimization module is further configured to determine the current optimization parameters based on the current optimal operating state and energy-saving effect; display the current optimization parameters to the user and request whether to perform automatic optimization; perform automatic optimization based on the current optimization parameters if the user selects automatic optimization; and perform optimization based on user input if the user does not select automatic optimization.

[0059] In some embodiments of this disclosure, the on-site energy management device further includes:

[0060] The data analysis module is configured to analyze at least one of the following data: current production task, current energy consumption data, and current energy-consuming equipment data, in at least one of the following dimensions: time dimension, region dimension, and equipment dimension.

[0061] The visualization module is also configured to display the analysis results to the user.

[0062] In some embodiments of this disclosure, the on-site energy management device further includes:

[0063] The instruction issuing module is configured to send an abnormal message to the field alarm device when the current device's energy consumption is abnormal, triggering the field alarm device to issue an on-site alarm.

[0064] According to another aspect of this disclosure, a computer device is provided, comprising:

[0065] Memory, used to store instructions;

[0066] A processor is configured to execute the instructions, causing the computer device to perform operations implementing the field energy management method as described in any of the above embodiments.

[0067] According to another aspect of this disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement the field energy management method as described in any of the above embodiments.

[0068] This disclosure, based on the edge side and the combination of production and energy consumption, can achieve visualized and accurate energy consumption assessment. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a schematic diagram of some embodiments of the field energy management method disclosed herein.

[0071] Figure 2 This is a schematic diagram of some other embodiments of the field energy management method disclosed herein.

[0072] Figure 3 This is a schematic diagram showing the working status and duration of the equipment under the production plan in some embodiments of this working condition.

[0073] Figure 4 This is a schematic diagram of some other embodiments of the field energy management method disclosed herein.

[0074] Figure 5 This is a schematic diagram of some further embodiments of the field energy management method disclosed herein.

[0075] Figure 6 This is a schematic diagram of the structure of some embodiments of the field energy management device disclosed herein.

[0076] Figure 7 This is a schematic diagram of the structure of some embodiments of the computer device disclosed herein. Detailed Implementation

[0077] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0078] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0079] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0080] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0081] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0082] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0083] The inventors discovered through research that current intelligent building systems collect energy consumption data but lack fine-grained analysis. Energy flow is unclear, and there are no scheduling or optimization strategies, leading to energy management relying primarily on the skills and experience of on-site personnel, resulting in unreliable energy-saving effects. Some intelligent building systems collect and analyze data, but this is mainly stored in the cloud, and there are no automatic energy-saving response strategies on-site. In the event of network anomalies, the cloud cannot retrieve data, causing data gaps, and energy-saving strategies cannot be distributed to devices, leading to abnormal strategy execution.

[0084] The technical problems with the related technologies are: 1. The disconnect between energy consumption analysis and production planning increases the difficulty of data analysis for management personnel. 2. The lack of direct, visualized energy management devices on-site makes it impossible for on-site personnel to quickly detect abnormal energy consumption and respond accordingly, or to adjust equipment operation strategies in a timely manner.

[0085] In view of at least one of the above technical problems, this disclosure provides a method and apparatus for on-site energy management, a computer device and a storage medium, which will be described below through specific embodiments.

[0086] Figure 1 This is a schematic diagram of some embodiments of the on-site energy management method of this disclosure. Preferably, this embodiment can be executed by the on-site energy management device or the computer equipment of this disclosure. Figure 1 As shown, the method may include at least one of steps 11 and 12, wherein:

[0087] Step 11: Obtain the current production task, current energy consumption data, and current energy-consuming equipment data.

[0088] In some embodiments of this disclosure, step 11 may include at least one of steps 111 to 112, wherein:

[0089] Step 111: Receive the current production task input by the management personnel through the visualization display module (visual interface).

[0090] Step 112: Collect current energy-consuming equipment data and current energy consumption data monitored by energy consumption monitoring equipment.

[0091] In some embodiments of this disclosure, step 11 may further include: acquiring energy consumption monitoring equipment data, wherein the energy consumption monitoring equipment data includes energy consumption data, power and current data of monitoring instruments, etc.

[0092] Step 12: Based on the current production task, current energy consumption data, and current energy-consuming equipment data, determine whether the current equipment energy consumption is abnormal.

[0093] In some embodiments of this disclosure, step 12 may include: importing the current production task, current energy consumption data, and current energy-consuming equipment data into a pre-trained equipment energy consumption assessment model to determine whether the current equipment energy consumption is abnormal.

[0094] In some embodiments of this disclosure, step 12 may include: determining a standard energy consumption curve based on the current production task and current energy-consuming equipment data; determining an actual energy consumption curve based on the current energy consumption data; determining the energy consumption deviation based on the actual energy consumption curve and the standard energy consumption curve; and determining whether the current equipment energy consumption is abnormal based on whether the energy consumption deviation is greater than a predetermined value.

[0095] In some embodiments of this disclosure, the on-site energy management method may further include: sending an abnormal message to an on-site alarm device when the current equipment energy consumption is abnormal, thereby triggering the on-site alarm device to issue an on-site alarm.

[0096] Figure 2 This is a schematic diagram of some other embodiments of the on-site energy management method of this disclosure. Preferably, this embodiment can be executed by the on-site energy management device or the computer equipment of this disclosure. Figure 2 As shown, the method (e.g.) Figure 1 Step 12) of the embodiment may include at least one of steps 121-126, wherein:

[0097] Step 121: Construct an equipment energy consumption assessment model.

[0098] In some embodiments of this disclosure, step 121 may include at least one of steps 1211-1213, wherein:

[0099] Step 1211: Establish a dynamic model for indicators and production plans.

[0100] In some embodiments of this disclosure, step 1211 may include: collecting the working status and duration of equipment under different production plans, and establishing a relationship function between the production plan and the equipment operating status.

[0101] Figure 3 This is a schematic diagram illustrating the working status and duration of equipment under the production plan in some embodiments of this operating condition. For example... Figure 3 As shown, assuming a production line has three production tasks: a, b, and c, the corresponding working times for the equipment used in these tasks are ta, tb, and tc, respectively. Figure 3As shown, the device operating states corresponding to the three tasks and the working time of each state are as follows: ta=ta1_1+ta1_2+ta2_1+ta2_2, tb=tb1_1+tb2_1+tb2_2+tb2_3, tc=tc1_1.

[0102] Step 1212: Establish the relationship function between energy consumption and runtime of different equipment operating states.

[0103] In some embodiments of this disclosure, the relationship between energy consumption and runtime of different device operating states is given by the function Q=F(T), where T is the time for each device in each state. Therefore, the total energy consumption of the overall production task is Q. 总 It equals the sum of energy consumption of all devices under different operating conditions.

[0104] In some embodiments of this disclosure, since the energy consumption per unit time is different for each device in each state, the total energy consumption Q of the overall production task is calculated. 总 This can include: first calculating the energy consumption of each device in each state, then calculating the total energy consumption of each state, and finally calculating the total energy consumption of all devices.

[0105] In some embodiments of this disclosure, Q=F(T) represents the correspondence between energy consumption and production tasks, equipment, equipment status, and runtime.

[0106] Step 1213: Based on the relationship between the equipment corresponding to the production task and the equipment's working status, establish an equipment energy consumption assessment model for production tasks and equipment energy consumption.

[0107] Step 122: Train the equipment energy consumption assessment model.

[0108] In some embodiments of this disclosure, step 122 may include: importing normal evaluation data for a predetermined time period to determine the model matching degree; if the energy consumption deviation is less than a predetermined value, the model is matched; if the energy consumption deviation is not less than the predetermined value, the model is adjusted, and then normal evaluation data is imported for training; after the model is matched, abnormal evaluation data is imported for training.

[0109] In some embodiments of this disclosure, step 122 may include: importing six months of normal data and checking the model's matching degree; if the deviation is within the positive or negative deviation range, the model matches; if it exceeds the deviation range, the model is adjusted, and normal data is imported again for training; after the model matches, abnormal data is imported again, and the same operation is performed. Finally, the trained model is output.

[0110] Step 123: Import the current production task, current energy consumption data, and current energy-consuming equipment data into the pre-trained equipment energy consumption assessment model to determine whether the current equipment energy consumption is abnormal.

[0111] In some embodiments of this disclosure, step 123 may include: importing real-time data into the device energy consumption assessment model, obtaining the model deviation, and determining that the energy consumption is abnormal when the deviation is greater than a predetermined value (the set allowable range).

[0112] In some embodiments of this disclosure, step 123 may include: importing actual production tasks and equipment operating status into the equipment energy consumption assessment model, calculating energy consumption, plotting the calculated energy consumption curve and the actual energy consumption curve, checking the degree of matching between the two curves, and calculating the deviation based on the calculated energy consumption and the actual energy consumption at each time.

[0113] In some embodiments of this disclosure, step 123 may include at least one of steps 1231-1234, wherein:

[0114] Step 1231: Determine the standard energy consumption curve based on the current production task and current energy-consuming equipment data.

[0115] Step 1232: Determine the actual energy consumption curve based on the current energy consumption data.

[0116] Step 1233: Determine the energy consumption deviation based on the actual energy consumption curve and the standard energy consumption curve.

[0117] Steps 1, 2, 3, and 4: Determine whether the current energy consumption of the equipment is abnormal based on whether the energy consumption deviation is greater than the predetermined value.

[0118] The embodiments disclosed above are based on the edge side and can achieve visualized and accurate energy consumption assessment by combining production and energy consumption.

[0119] The above embodiments of this disclosure establish an energy consumption anomaly diagnosis mechanism based on dynamic index evaluation. This mechanism can locate abnormal energy consumption ranges and achieve on-site alarms by dynamically adjusting production tasks and energy-saving indicators by on-site personnel.

[0120] Figure 4 This is a schematic diagram of some other embodiments of the on-site energy management method of this disclosure. Preferably, this embodiment can be executed by the on-site energy management device or the computer equipment of this disclosure. Figure 4 As shown, the on-site energy management method disclosed herein may include, in addition to, Figure 1 or Figure 2 In addition to the steps in the embodiment, it may also include at least one of steps 41 to 46, wherein:

[0121] Step 41: Determine the current optimal operating state and energy-saving effect based on the current energy consumption data and the current energy-consuming equipment data.

[0122] In some embodiments of this disclosure, step 41 may include at least one of steps 411-414, wherein:

[0123] Step 411: Establish an energy consumption model for the equipment under different operating conditions and states.

[0124] In some embodiments of this disclosure, the operating conditions include different loads, different production tasks, etc.

[0125] In some embodiments of this disclosure, step 411 may include: long-term collection of equipment operating condition data, operating status, and energy consumption; finding the relationship between operating condition, operating status, and energy consumption; plotting the relationship as a curve; finding the optimal operating parameters of the equipment under different operating conditions; and calculating the energy consumption of the equipment under these operating parameters.

[0126] In some embodiments of this disclosure, step 411 may include: establishing a separate energy consumption model for each device, each operating condition, and each state.

[0127] In some embodiments of this disclosure, the operating conditions include: full-load production conditions, shutdown conditions, and medium-load conditions.

[0128] In some embodiments of this disclosure, the operating data includes various operating states of the device, such as start / stop, mode, frequency, differential pressure, temperature, etc.

[0129] Step 412: Cluster the fault data and operating condition data, and select training data according to different energy consumption models.

[0130] In some embodiments of this disclosure, step 412 may include: clustering data with the same or similar faults and operating conditions.

[0131] In some embodiments of this disclosure, step 412 may include: setting different labels for all data such as operating conditions and running data, filtering data according to operating condition labels, thereby classifying the data.

[0132] Step 413: Train the energy consumption model using training data, plot the model evaluation value and actual running value on a line graph, and check the model reliability by observing the trend.

[0133] In some embodiments of this disclosure, step 413, the step of checking the model reliability by trend, may include: checking the closeness between the actual value and the deviation value; the smaller the mean square error and the closer they are, the higher the accuracy and the more reliable the model.

[0134] Step 414: Import the current energy consumption data and current energy-consuming equipment data into the pre-trained energy consumption model to determine the current optimal operating state and energy-saving effect.

[0135] In some embodiments of this disclosure, step 414 may include: placing the trained energy consumption model in the field energy management device and calculating the optimal operating state and energy-saving effect based on the current situation.

[0136] In some embodiments of this disclosure, step 414 may include: collecting real-time data, wherein the real-time data includes current device operating data and current energy consumption data; plotting an actual energy consumption curve based on the current energy consumption data; putting the current device operating data into an energy consumption model, automatically determining the current operating condition, and outputting the optimal operating state and the energy-saving effect of operating under that operating state.

[0137] Step 42: Show the user the current optimal operating status and energy-saving effect.

[0138] Step 43: Determine the current optimization parameters based on the current optimal operating status and energy-saving effect.

[0139] Step 44: Show the user the current tuning parameters and ask whether to perform automatic tuning.

[0140] In some embodiments of this disclosure, step 46 may include: displaying a pop-up interface to the user so that the user can choose whether to automatically optimize or manually distribute the data.

[0141] Step 45: If the user selects automatic tuning, perform automatic tuning based on the current tuning parameters.

[0142] In some embodiments of this disclosure, step 45 may include: having an option on the interface for automatic tuning; if selected, automatic control is performed according to the model calculation results, that is, tuning is performed according to the current tuning parameters calculated by the model.

[0143] Step 46: If the user does not select automatic tuning, perform tuning based on the user input.

[0144] In some embodiments of this disclosure, step 46 may include: if automatic tuning is not selected, the administrator can view the current tuning parameter suggestions, manually change the parameter values ​​or not change them, and click the current control parameters below the issue button.

[0145] In some embodiments of this disclosure, step 46 may include: if automatic tuning is not selected, i.e., manual tuning is selected, displaying control content (current tuning parameter suggestions) so that the administrator can manually click to issue.

[0146] In some embodiments of this disclosure, the on-site energy management method may include: If the air compressor starts and stops frequently, monitoring operating parameters such as temperature, oil temperature, oil pressure, current, and voltage, and calculating that the differential pressure needs to be set to a specific differential pressure X, then displaying the current start / stop frequency as a specific frequency XX on the interface, and adjusting the differential pressure to the specific differential pressure X. If the automatic optimization function is currently enabled, the command is issued directly; otherwise, a pop-up window prompts the administrator to confirm whether to issue the command.

[0147] The embodiments disclosed above establish a real-time optimization AI self-control model based on on-site control strategy switching, which can provide functions such as energy-saving strategy recommendation and one-click distribution, automatic optimization, etc., to realize on-site energy-saving diagnosis and strategy optimization.

[0148] Figure 5 This is a schematic diagram illustrating further embodiments of the on-site energy management method of this disclosure. Preferably, this embodiment can be executed by the on-site energy management device or the computer equipment of this disclosure. Figure 5 As shown, the on-site energy management method disclosed herein may include, in addition to, Figures 1-3 In addition to the steps of at least one embodiment, the method may also include at least one step from steps 51 to 52, wherein:

[0149] Step 51: Analyze at least one of the following data: current production task, current energy consumption data, and current energy-consuming equipment data, in at least one of the following dimensions: time dimension, region dimension, and equipment dimension.

[0150] In some embodiments of this disclosure, step 51 may include: parsing the data acquired by the data acquisition module, obtaining the required data for analysis, including data change trends, year-on-year comparisons, month-on-month comparisons, etc.; and performing analysis according to the dimensions of data acquisition, such as time dimension, interval dimension, and device dimension, to achieve data comparison across different dimensions.

[0151] Step 52: Present the analysis results to the user.

[0152] In some embodiments of this disclosure, step 51 may include: displaying the analysis results in a visualization module in the form of charts.

[0153] The embodiments disclosed above are based on the edge side, combining production and energy consumption, and can achieve comprehensive energy analysis.

[0154] The above embodiments of this disclosure provide an intelligent building on-site energy management method, which provides a comprehensive energy analysis model that combines production and energy consumption at the edge and an energy consumption anomaly diagnosis mechanism based on dynamic index evaluation. It provides precise energy consumption assessment, efficient energy consumption anomaly diagnosis and real-time optimization AI self-control, and realizes fine-grained energy analysis, precise energy consumption positioning and automatic optimization of energy-saving strategies.

[0155] The embodiments disclosed above are based on a comprehensive energy analysis model that combines edge-side analysis, production, and energy consumption, enabling visualized and accurate energy consumption assessment.

[0156] Figure 6 This is a schematic diagram of the structure of some embodiments of the field energy management device disclosed herein. For example... Figure 6As shown, the field energy management device disclosed herein may include a visualization module 61, a data acquisition module 62, and an anomaly diagnosis module 63, wherein:

[0157] The visualization module 61 is configured to obtain the current production task.

[0158] In some embodiments of this disclosure, the visualization module 61 can be configured to receive current production tasks input by the administrator.

[0159] The data acquisition module 62 is configured to acquire current energy consumption data and current energy-consuming device data.

[0160] In some embodiments of this disclosure, the data acquisition module 62 can be configured to acquire real-time energy-consuming device data and real-time energy consumption monitoring device data through a communication interface and provide them to the data analysis module.

[0161] In some embodiments of this disclosure, the energy consumption monitoring equipment data includes energy consumption data, power and current data of the monitoring instruments, etc.

[0162] In some embodiments of this disclosure, the communication interface includes, but is not limited to, an RS485 port, an I / O interface, and an Ethernet port.

[0163] The anomaly diagnosis module 63 is configured to determine whether the energy consumption of the current equipment is abnormal based on the current production task, current energy consumption data, and current energy-consuming equipment data.

[0164] In some embodiments of this disclosure, the anomaly diagnosis module 63 can be configured to determine a standard energy consumption curve based on the current production task and the current energy-consuming equipment data; determine an actual energy consumption curve based on the current energy consumption data; determine the energy consumption deviation based on the actual energy consumption curve and the standard energy consumption curve; and determine whether the current equipment energy consumption is abnormal based on whether the energy consumption deviation is greater than a predetermined value.

[0165] In some embodiments of this disclosure, the anomaly diagnosis module 63 can be configured to construct an equipment energy consumption assessment model; train the equipment energy consumption assessment model; import the current production task, current energy consumption data, and current energy-consuming equipment data into the pre-trained equipment energy consumption assessment model to determine whether the current equipment energy consumption is abnormal.

[0166] In some embodiments of this disclosure, the anomaly diagnosis module 63 can be configured to, when constructing an equipment energy consumption assessment model, collect the working status and duration of equipment under different production plans, establish a relationship function between production plans and equipment operating status; establish a relationship function between energy consumption and operating duration of different equipment operating states; and establish an equipment energy consumption assessment model between production tasks and equipment energy consumption based on the relationship between the equipment corresponding to the production tasks and the equipment operating status.

[0167] In some embodiments of this disclosure, the anomaly diagnosis module 63 can be configured to, while training the device energy consumption assessment model, import normal assessment data for a predetermined time period to determine the model matching degree; if the energy consumption deviation is less than a predetermined value, the model matches; if the energy consumption deviation is not less than the predetermined value, the model is adjusted, and normal assessment data is imported for training again; after the model matches, abnormal assessment data is imported for training again.

[0168] In some embodiments of this disclosure, the anomaly diagnosis module 63 can be configured to calculate whether the current actual energy consumption and production match based on the indicators set by the user and the input production plan. If they do not match, there is an anomaly. At this time, the anomaly message is sent to the instruction issuing module to control the on-site audible and visual alarm devices to set up an on-site alarm.

[0169] In some embodiments of this disclosure, such as Figure 6 As shown, the field energy management device may further include a command issuing module 66, wherein:

[0170] The instruction issuing module 66 is configured to send an abnormal message to the field alarm device when the current device has abnormal energy consumption, thereby triggering the field alarm device to issue an alarm.

[0171] In some embodiments of this disclosure, the instruction issuing module 66 can be configured to convert instructions issued by other modules into device-recognizable data and issue them to the device for execution.

[0172] The embodiments disclosed above, based on the edge side and the combination of production and energy consumption, can achieve visualized and accurate energy consumption assessment.

[0173] The above embodiments of this disclosure establish an energy consumption anomaly diagnosis mechanism based on dynamic index evaluation. This mechanism can locate abnormal energy consumption ranges and achieve on-site alarms by dynamically adjusting production tasks and energy-saving indicators by on-site personnel.

[0174] In some embodiments of this disclosure, such as Figure 6 As shown, the field energy management device may further include a real-time optimization module 64, wherein:

[0175] The real-time optimization module 64 is configured to determine the current optimal operating state and energy-saving effect based on the current energy consumption data and the current energy-consuming equipment data.

[0176] The visualization module 61 can also be configured to display the current optimal operating status and energy-saving effect to the user.

[0177] In some embodiments of this disclosure, the real-time optimization module 64 can be configured to establish energy consumption models of the equipment under different operating conditions and running states; cluster fault data and operating condition data, and filter training data according to different energy consumption models; train the energy consumption model using the training data; import the current energy consumption data and the current energy-consuming equipment data into the pre-trained energy consumption model to determine the current optimal operating state and energy-saving effect.

[0178] In some embodiments of this disclosure, the real-time optimization module 64 may also be configured to determine the current optimization parameters based on the current optimal operating state and energy-saving effect; display the current optimization parameters to the user and request whether to perform automatic optimization; perform automatic optimization based on the current optimization parameters if the user selects automatic optimization; and perform optimization based on user input if the user does not select automatic optimization.

[0179] In some embodiments of this disclosure, the real-time optimization module 64 can also be configured to display a prompt on the visualization module based on the anomaly diagnosis results, allowing administrators to confirm whether an automatic policy needs to be activated or a policy can be executed with one click, with the policy content visible. If execution is required, the instruction is sent to the instruction distribution module and distributed to the energy-consuming devices.

[0180] The embodiments disclosed above establish a real-time optimization AI self-control model based on on-site control strategy switching, which can provide functions such as energy-saving strategy recommendation and one-click distribution, automatic optimization, etc., to realize on-site energy-saving diagnosis and strategy optimization.

[0181] In some embodiments of this disclosure, such as Figure 6 As shown, the on-site energy management device may further include a data analysis module 65, wherein:

[0182] The data analysis module 65 is configured to analyze at least one of the following data: current production task, current energy consumption data, and current energy-consuming equipment data, in at least one of the following dimensions: time dimension, region dimension, and equipment dimension.

[0183] In some embodiments of this disclosure, the visualization module 61 may also be configured to display the analysis results to the user.

[0184] In some embodiments of this disclosure, the data analysis module 65 can be configured to parse the data acquired by the data acquisition module, obtain the required data for analysis, and the analysis content includes data change trends, year-on-year comparisons, month-on-month comparisons, etc.; according to the dimensions of data acquisition, the analysis is performed according to the time dimension, interval dimension, and device dimension to achieve data comparison in different dimensions, wherein the analysis results are reflected in the visualization display module and displayed in the form of charts.

[0185] In some embodiments of this disclosure, the visualization module 61 can be configured to display the real-time status of the device, real-time energy consumption data, energy consumption analysis charts, and optimization strategies, and provide control functions such as device control, strategy distribution, and automatic strategy switching.

[0186] In some embodiments of this disclosure, the optimization strategy is the control scheme given after the energy consumption diagnosis and energy consumption model calculation of this disclosure.

[0187] The embodiments disclosed above are based on the edge side, combining production and energy consumption, and can achieve comprehensive energy analysis.

[0188] The above embodiments of this disclosure provide an intelligent building on-site energy management device, which provides a comprehensive energy analysis model that combines production and energy consumption at the edge and an energy consumption anomaly diagnosis mechanism based on dynamic index evaluation. It provides precise energy consumption assessment, efficient energy consumption anomaly diagnosis, and real-time optimization AI self-control, realizing fine-grained energy analysis, precise energy consumption positioning, and automatic optimization of energy-saving strategies.

[0189] Figure 7 This is a schematic diagram illustrating the structure of some embodiments of the computer device disclosed herein. For example... Figure 7 As shown, the computer device includes a memory 71 and a processor 72.

[0190] Memory 71 is used to store instructions, and processor 72 is coupled to memory 71. Processor 72 is configured to execute instructions stored in memory to implement any of the above embodiments (e.g., Figure 6 The method involved in the embodiment).

[0191] like Figure 7 As shown, the computer device also includes a communication interface 73 for exchanging information with other devices. Additionally, the computer device includes a bus 74, through which the processor 72, communication interface 73, and memory 71 communicate with each other.

[0192] The memory 71 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive. The memory 71 may also be a memory array. The memory 71 may also be divided into blocks, and these blocks may be combined into virtual volumes according to certain rules.

[0193] Furthermore, processor 72 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present disclosure.

[0194] The embodiments disclosed above are based on a comprehensive energy analysis model that combines edge-side analysis, production, and energy consumption, enabling visualized and accurate energy consumption assessment.

[0195] According to another aspect of this disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement any of the embodiments described above (e.g., Figures 1-5 The on-site energy management method described in any embodiment.

[0196] In some embodiments of this disclosure, the computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0197] The embodiments disclosed above are based on a comprehensive energy analysis model that combines edge-side analysis, production, and energy consumption, enabling visualized and accurate energy consumption assessment.

[0198] The above embodiments of this disclosure establish an energy consumption anomaly diagnosis mechanism based on dynamic index evaluation. This mechanism can locate abnormal energy consumption ranges and trigger on-site alarms by dynamically adjusting production tasks and energy-saving indicators by on-site personnel.

[0199] The embodiments disclosed above establish a real-time optimization AI self-control model based on on-site control strategy switching, which can provide functions such as energy-saving strategy recommendation and one-click distribution, automatic optimization, etc., to realize on-site energy-saving diagnosis and strategy optimization.

[0200] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, apparatus, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0201] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0202] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0203] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0204] The computer equipment, field energy management device, visualization module, data acquisition module, anomaly diagnosis module, instruction issuance module, real-time optimization module, and data analysis module described above can be implemented as a general-purpose processor, programmable logic controller (PLC), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described in this application.

[0205] This disclosure has now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

[0206] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing the relevant hardware to implement them. The program can be stored in a non-transitory computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0207] The description in this disclosure is provided for illustrative and descriptive purposes only and is not intended to be exhaustive or to limit the disclosure to its forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of this disclosure and to enable those skilled in the art to understand this disclosure and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. An on-site energy management method, comprising: Obtain current production tasks, current energy consumption data, and current energy-consuming equipment data; Based on the current production tasks, current energy consumption data, and current energy-consuming equipment data, determine whether the current equipment energy consumption is abnormal; Based on current energy consumption data and current energy-consuming equipment data, determine the current optimal operating state and energy-saving effect. The process of determining the current optimal operating state and energy-saving effect based on current energy consumption data and current energy-consuming equipment data includes: importing the current energy consumption data and current energy-consuming equipment data into a pre-trained energy consumption model to determine the current optimal operating state and energy-saving effect. Show users the current optimal operating status and energy-saving effect; Determine the current optimization parameters based on the current optimal operating status and energy-saving effect; Display the current tuning parameters to the user and ask whether to perform automatic tuning; If the user selects automatic tuning, automatic tuning will be performed based on the current tuning parameters; Optimize based on user input if the user does not select automatic optimization; The step of determining whether the current equipment energy consumption is abnormal based on the current production task, current energy consumption data, and current energy-consuming equipment data includes: Import the current production task, current energy consumption data, and current energy-consuming equipment data into the pre-trained equipment energy consumption assessment model to determine whether the current equipment energy consumption is abnormal. The on-site energy management method further includes: Develop an equipment energy consumption assessment model; The energy consumption assessment model for the constructed equipment includes: Collect the working status and duration of equipment under different production plans, and establish a relationship function between production plans and equipment operating status; Establish a function relating energy consumption and runtime under different operating states of equipment; Based on the relationship between production tasks and the corresponding equipment and equipment operating status, establish an equipment energy consumption assessment model for production tasks and equipment energy consumption. The on-site energy management method further includes: Establish energy consumption models for equipment under different operating conditions and states; Cluster fault data and operating condition data, and select training data according to different energy consumption models; The energy consumption model is trained using training data.

2. The on-site energy management method according to claim 1, wherein, The step of determining whether the current equipment energy consumption is abnormal based on the current production task, current energy consumption data, and current energy-consuming equipment data includes: Determine the standard energy consumption curve based on the current production tasks and current energy-consuming equipment data; Determine the actual energy consumption curve based on current energy consumption data; Determine the energy consumption deviation based on the actual energy consumption curve and the standard energy consumption curve; Whether the energy consumption deviation is greater than the predetermined value determines whether the current energy consumption of the equipment is abnormal.

3. The on-site energy management method according to claim 1 or 2, wherein, The on-site energy management method also includes: Train the equipment energy consumption assessment model.

4. The on-site energy management method according to claim 3, wherein, The training of the equipment energy consumption assessment model includes: Import normal evaluation data for a predetermined time period to determine the degree of model matching; If the energy consumption deviation is less than the predetermined value, then the model matches; If the energy consumption deviation is not less than the predetermined value, the model is adjusted and then the normal evaluation data is imported for training. After model matching, evaluation outlier data is imported for training.

5. The on-site energy management method according to claim 1 or 2 further includes: Analyze at least one of the following data: current production tasks, current energy consumption data, and current energy-consuming equipment data, in at least one of the following dimensions: time dimension, region dimension, and equipment dimension. The analysis results are presented to the user.

6. The on-site energy management method according to claim 1 or 2 further includes: In the event of abnormal power consumption of the current equipment, an abnormal message will be sent to the on-site alarm device, triggering the on-site alarm device to issue an on-site alarm.

7. A field energy management device, comprising: The visualization module is configured to retrieve the current production tasks; The data acquisition module is configured to acquire current energy consumption data and current energy-consuming device data; The anomaly diagnosis module is configured to determine whether the energy consumption of the current equipment is abnormal based on the current production task, current energy consumption data, and current energy-consuming equipment data. The real-time optimization module is configured to determine the current optimal operating state and energy-saving effect based on current energy consumption data and current energy-consuming equipment data; determine the current optimization parameters based on the current optimal operating state and energy-saving effect; perform automatic optimization based on the current optimization parameters if the user selects automatic optimization; and perform optimization based on user input if the user does not select automatic optimization. The visualization module is also configured to display the current optimal operating status and energy-saving effect to the user; show the user the current optimization parameters; and ask whether to perform automatic optimization. The anomaly diagnosis module is configured to build an equipment energy consumption assessment model; import the current production task, current energy consumption data and current energy-consuming equipment data into the pre-trained equipment energy consumption assessment model to determine whether the current equipment energy consumption is abnormal. The anomaly diagnosis module is configured to, in the case of building an equipment energy consumption assessment model, collect the working status and duration of equipment under different production plans, establish a relationship function between production plans and equipment operating status; establish a relationship function between energy consumption and operating duration of different equipment operating states; and establish an equipment energy consumption assessment model between production tasks and equipment energy consumption based on the relationship between production tasks and equipment operating status. The real-time optimization module is also configured to establish energy consumption models for equipment under different operating conditions and states; cluster fault data and operating condition data, and select training data according to different energy consumption models; train the energy consumption model using the training data; and import current energy consumption data and current energy-consuming equipment data into the pre-trained energy consumption model to determine the current optimal operating state and energy-saving effect.

8. The on-site energy management device according to claim 7, wherein: The anomaly diagnosis module is configured to determine the standard energy consumption curve based on the current production task and current energy-consuming equipment data; determine the actual energy consumption curve based on the current energy consumption data; determine the energy consumption deviation based on the actual energy consumption curve and the standard energy consumption curve; and determine whether the current equipment energy consumption is abnormal based on whether the energy consumption deviation is greater than a predetermined value.

9. The on-site energy management device according to claim 7 or 8, wherein: The anomaly diagnosis module is configured to train the equipment energy consumption assessment model.

10. The field energy management device according to claim 9, wherein: The anomaly diagnosis module is configured to import normal evaluation data within a predetermined time period to determine the model's matching degree when training the equipment energy consumption assessment model; if the energy consumption deviation is less than a predetermined value, the model matches; if the energy consumption deviation is not less than the predetermined value, the model is adjusted, and normal evaluation data is imported for training again; after the model matches, abnormal evaluation data is imported for training again.

11. The on-site energy management device according to claim 7 or 8, further comprising: The data analysis module is configured to analyze at least one of the following data: current production task, current energy consumption data, and current energy-consuming equipment data, in at least one of the following dimensions: time dimension, region dimension, and equipment dimension. The visualization module is also configured to display the analysis results to the user.

12. The on-site energy management device according to claim 7 or 8, further comprising: The instruction issuing module is configured to send an abnormal message to the field alarm device when the current device's energy consumption is abnormal, triggering the field alarm device to issue an on-site alarm.

13. A computer device, comprising: Memory, used to store instructions; A processor for executing the instructions, causing the computer device to implement the field energy management method as described in any one of claims 1-6.

14. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the field energy management method as described in any one of claims 1-6.

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