Energy management control system and method based on cloud platform

Abstract

The invention relates to the technical field of cloud platforms, in particular to an energy management control system and method based on a cloud platform, and the system comprises an energy data normalizing module, a series current transformer, a parallel voltage sensor, an energy management module and an energy management module, a continuous analog signal is periodically sampled and quantized to convert a running current value and a line voltage value into digital signals, and then a voltage effective value and a current effective value under the same timestamp are calculated to obtain a real-time power value. In the invention, a strategy generation mode combining internal analysis and external economic factors is adopted, so that a control instruction is not a fixed start-stop operation any more, but is a dynamic operation optimization instruction set with optimal economical efficiency, and finally, an instruction containing specific parameters is sent to the intelligent relay and the variable frequency controller for control; and meanwhile, due to continuous power safety threshold comparison, the real-time abnormity warning capability is provided for equipment operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud platform, and particularly relates to an energy management control system and method based on a cloud platform. BACKGROUND

[0002] The technical field of cloud platform is a network-based computing architecture, and its core is to provide scalable, elastic computing resources, storage capabilities and application services through the Internet.

[0003] The existing cloud platform technology can receive and store massive data from terminal devices, but it is difficult to autonomously analyze the internal meaning of these data, such as how current and voltage values reflect the running efficiency of the device. In actual application, a general cloud platform can serve as a storage warehouse for energy data, but it is difficult to automatically identify specific energy waste phenomena such as device no-load and efficiency decay. For example, the platform can record the running power data of a production line motor for several hours, but it cannot determine whether this period of time is effective production or no-load waste. In addition, the existing technology mainly focuses on data aggregation and processing, and lacks a standardized closed-loop mechanism in terms of issuing and executing control instructions. Even if problems are found through manual analysis, the general cloud platform cannot directly generate and send targeted control instructions to adjust the frequency of a specific frequency converter or disconnect a relay at a specific time. Its function is limited to passive data display and storage. Therefore, improvements are needed. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art and to provide an energy management control system and method based on a cloud platform.

[0005] To achieve the above purpose, the present application adopts the following technical solution: an energy management control system based on a cloud platform comprises:

[0006] An energy data normalization module, a current transformer is connected in series to collect the running current value of the production line motor, and a voltage sensor is connected in parallel to monitor the line voltage value. The running current value and the line voltage value are converted into digital signals by periodically sampling and quantizing the continuous analog signals. Then, the real-time power value is obtained by operating the voltage effective value and the current effective value under the same timestamp. The energy consumption value is obtained by accumulating the real-time power value in unit time. The device energy consumption time series data stream is obtained by combining the real-time power value, the energy consumption value, the device identifier and the collection timestamp.

[0007] The energy consumption feature analysis module is configured to set a lower limit threshold of the running power of the air conditioning system according to the device energy consumption time sequence data stream, determine a continuous time period in which the real-time power value in the device energy consumption time sequence data stream is lower than the lower limit threshold of the running power as an idle time period, record the start time stamp and the end time stamp of the idle time period, calculate the time difference to obtain the duration, count the number of occurrences of the idle time period in a unit period to obtain the occurrence frequency, compare the historical same-period energy consumption value with the current energy consumption value, calculate the difference between the historical same-period energy consumption value and the current energy consumption value, and the ratio of the historical same-period energy consumption value to obtain the energy consumption fluctuation rate, and establish the energy consumption waste link identification set.

[0008] Preferably, the system further comprises:

[0009] The device control strategy module is configured to retrieve a preset time-of-use electricity price interval according to the energy consumption waste link identification set, perform time matching on the identified idle time period and the electricity price peak time period, migrate non-production tasks to the electricity price valley time period to form a production line motor preferred running time period, and reversely adjust the reference frequency of the air conditioning system frequency converter according to the energy consumption fluctuation rate value in the energy consumption waste link identification set, calculate the target frequency value, set the running start-stop time point, and generate a device running optimization instruction set.

[0010] The remote instruction execution module is configured to send a start-stop instruction including a device address and a switch state bit to an intelligent relay and send a frequency adjustment instruction including a target frequency value to a frequency conversion controller according to the device running optimization instruction set, and continuously extract a real-time power value in the device energy consumption time sequence data stream and compare the real-time power value with a preset power safety threshold in a time segment, generate structured data including a device identification, an abnormal type, and a time stamp to obtain a device abnormal state alarm signal when the real-time power value exceeds the power safety threshold.

[0011] Preferably, the energy data regularization module comprises:

[0012] The electric signal digitization submodule is configured to acquire production line motor running current values through a series-connected current transformer and monitor line voltage values through a parallel-connected voltage sensor, discretely sample continuous analog signals by setting a fixed sampling frequency, map the amplitude of each sampling point to a preset discrete level value, convert the running current values and the line voltage values into digital signals, and obtain a quantized electric signal sequence.

[0013] The energy consumption value calculation submodule is configured to extract voltage effective values and current effective values under the same time stamp and perform operations to obtain real-time power values according to the quantized electric signal sequence, perform integral accumulation operations on a plurality of real-time power values in a preset unit time to obtain energy consumption values, and obtain an instantaneous electric energy parameter set.

[0014] The time sequence data integration sub-module arranges real-time power values, energy consumption values, device identifiers and collection time stamps as independent fields in time sequence to construct a two-dimensional data table with time sequence structure and obtain device energy consumption time sequence data stream.

[0015] Preferably, the energy consumption feature analysis module comprises:

[0016] The no-load period determination sub-module sets a lower limit threshold of running power of the air conditioning system, traverses the real-time power values in the device energy consumption time sequence data stream point by point, and marks a time range corresponding to adjacent data points continuously satisfying the condition of being lower than the lower limit threshold of running power as a no-load period, and establishes device no-load period data.

[0017] The energy consumption fluctuation calculation sub-module retrieves the start time stamp and the end time stamp of the no-load period according to the device no-load period data, calculates the difference between the two to obtain the duration, and then calculates the occurrence frequency of the no-load period in a preset unit period to obtain the energy consumption fluctuation rate, and obtains the energy consumption fluctuation rate.

[0018] The waste link calibration sub-module extracts energy consumption values corresponding to the current time period from the historical database according to the energy consumption feature statistical value, calculates the difference between the historical same-period energy consumption value and the current energy consumption value, and then divides the energy consumption fluctuation rate by the historical same-period energy consumption value to obtain the energy consumption fluctuation rate, and integrates the duration, occurrence frequency and energy consumption fluctuation rate to establish the energy consumption waste link identification set.

[0019] Preferably, the device control strategy module comprises:

[0020] The motor period optimization sub-module retrieves a preset time-of-use electricity price interval according to the energy consumption waste link identification set, calculates the overlap degree between the identified no-load period and the electricity price peak period, and schedules the non-production task with the highest overlap degree to the electricity price valley period for execution to form a motor optimal operation schedule.

[0021] The variable frequency parameter adjustment sub-module performs linear combination operation on the energy consumption fluctuation rate value in the energy consumption waste link identification set and a preset reference frequency value, inversely calculates a target frequency value negatively associated with the energy consumption fluctuation rate, and sets the running start and stop time points of the air conditioning system to generate air conditioning system control parameters.

[0022] Preferably, the device control strategy module further comprises:

[0023] The instruction set generation sub-module converts the start and end time points of the optimal operation period into binary start and stop control bits according to the motor optimal operation schedule and the air conditioning system control parameters, encodes the target frequency value and the running start and stop time points into byte stream data to generate a device operation optimization instruction set.

[0024] Preferably, the remote instruction execution module comprises:

[0025] The instruction parsing and sending submodule parses the device address field in the device operation optimization instruction set to locate the intelligent relay and sends the binary switch state bit, and simultaneously sends the encoded target frequency value byte stream to the frequency converter controller to obtain the device control electrical signal according to the device operation optimization instruction set.

[0026] The real-time energy consumption monitoring submodule continuously extracts the real-time power value in the device energy consumption time sequence data stream, and according to the current timestamp, matches the corresponding time-sharing period preset power safety threshold value, compares the two values in size, and obtains the power overrun judgment result.

[0027] Preferably, the remote instruction execution module further comprises:

[0028] The abnormal alarm generation submodule, according to the power overrun judgment result, when the judgment result is a Boolean true value, immediately locks the current device identifier, timestamp and preset abnormal type code, and combines the three data into a fixed format data packet to obtain the device abnormal state alarm signal.

[0029] The application also provides an energy management control method, comprising the following steps:

[0030] The running current value of the production line motor is collected, the line voltage value is monitored, the running current value and the line voltage value are converted into digital signals through periodic sampling and quantization processing of continuous analog signals, then the voltage effective value and the current effective value under the same timestamp are operated to obtain the real-time power value, and the real-time power value in a unit time is accumulated to obtain the energy consumption value, the real-time power value, the energy consumption value, the device identifier and the collection timestamp are combined to obtain the device energy consumption time sequence data stream.

[0031] According to the device energy consumption time sequence data stream, the running power lower limit threshold of the air conditioning system is set, the continuous time period in which the real-time power value in the device energy consumption time sequence data stream is lower than the running power lower limit threshold is determined as the idle time period, the start timestamp and the end timestamp of the idle time period are recorded, the time difference value is calculated to obtain the continuous duration, the occurrence frequency of the idle time period in a unit period is counted to obtain the occurrence frequency, the historical same period energy consumption value and the current energy consumption value are compared, the difference value between the historical same period energy consumption value and the current energy consumption value is calculated, and the energy consumption fluctuation rate is obtained by the ratio of the historical same period energy consumption value, and an energy consumption waste link identification set is established.

[0032] According to the energy waste link identification set, a preset time-of-use electricity price interval is searched, the identified no-load period is time-matched with the electricity price peak period, non-production tasks are migrated to the electricity price valley period, a production line motor optimal operation period is formed, and according to the energy fluctuation rate value in the energy waste link identification set, the reference frequency of the air conditioning system frequency converter is reversely adjusted, a target frequency value is calculated, an operation start-stop time point is set, and a device operation optimization instruction set is generated.

[0033] According to the device operation optimization instruction set, a start-stop instruction including a device address and a switch state bit is sent to an intelligent relay, a frequency adjustment instruction including a target frequency value is sent to a frequency conversion controller, and at the same time, real-time power values in the device energy consumption time sequence data stream are continuously extracted, and are compared with a power safety threshold preset in a time interval, when the real-time power value exceeds the power safety threshold, structured data including a device identification, an abnormal type and a time stamp is generated, and a device abnormal state alarm signal is obtained.

[0034] Compared with the prior art, the advantages and positive effects of the present application are that:

[0035] In the present application, the collected production line motor operation current values and line voltage values are periodically sampled and quantitatively processed, and are structured and integrated in combination with time stamps and device identifications to form a device energy consumption time sequence data stream, which provides a standardized data basis for analysis and control. Based on the data stream, a lower limit threshold of the air conditioning system operation power is set to determine the no-load period, and the duration, frequency of occurrence and energy fluctuation rate are calculated, which can quantitatively and identify specific energy waste links, change passive data monitoring to active efficiency diagnosis, further time-match the identified waste links with the time-of-use electricity price interval, and formulate a production line motor optimal operation period in which non-production tasks are migrated to the electricity price valley period, and adjust the frequency of the air conditioning system frequency converter according to the energy fluctuation rate. This combined internal analysis and external economic factor strategy generation method makes the control instruction no longer a fixed start-stop operation, but a dynamic and economically optimal operation optimization instruction set, which finally sends the instruction containing specific parameters to the intelligent relay and the frequency conversion controller for control, and continuously compares the power safety threshold to provide real-time abnormal alarm capability for device operation. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The system flowchart of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0038] Referring to Figure 1 The application provides a technical solution: an energy management control system based on a cloud platform comprises:

[0039] An energy data regularization module, a series current transformer collects production line motor operating current values, and a parallel voltage sensor monitors line voltage values, converts operating current values and line voltage values into digital signals through periodic sampling and quantization processing of continuous analog signals, and then obtains real-time power values by operating voltage effective values and current effective values under the same timestamp, and obtains energy consumption values by cumulative operation of real-time power values in a unit time, combines real-time power values, energy consumption values, device identifiers, and collection timestamps to obtain device energy consumption time series data streams;

[0040] An energy consumption feature analysis module, configured to set an air conditioning system operating power lower limit threshold according to the device energy consumption time series data streams, determine a continuous time period in which the real-time power value is lower than the operating power lower limit threshold in the device energy consumption time series data streams as an idle time period, record the start timestamp and the end timestamp of the idle time period, calculate the time difference value to obtain the duration, count the occurrence frequency of the idle time period in a unit period, compare the historical same period energy consumption value with the current energy consumption value, calculate the difference value between the historical same period energy consumption value and the current energy consumption value, and obtain the energy consumption fluctuation rate by the ratio of the historical same period energy consumption value, and establish an energy consumption waste link identification set;

[0041] A device control strategy module, configured to retrieve a preset time-of-use electricity price interval according to the energy consumption waste link identification set, time-match the identified idle time period and the electricity price peak time period, migrate non-production tasks to the electricity price valley time period to form a production line motor optimal operating time period, and inversely adjust the reference frequency of the air conditioning system frequency converter according to the energy consumption fluctuation rate value in the energy consumption waste link identification set, calculate to obtain a target frequency value, set operating start and stop time points, and generate a device operation optimization instruction set;

[0042] A remote instruction execution module, configured to send start-stop instructions including device addresses and switch state bits to intelligent relays, and send frequency adjustment instructions including target frequency values to frequency conversion controllers according to the device operation optimization instruction set, and simultaneously continuously extract real-time power values in the device energy consumption time series data streams, and compare the real-time power values with time-of-use preset power safety thresholds, generate structured data including device identifiers, abnormal types, and timestamps when the real-time power values exceed the power safety thresholds, and obtain device abnormal state alarm signals.

[0043] The energy data regularization module comprises:

[0044] The electric signal digitization sub-module acquires the production line motor operating current value through the series current transformer and monitors the line voltage value through the parallel voltage sensor, discretely samples the continuous analog signal by setting a fixed sampling frequency, maps the amplitude of each sampling point to a preset discrete level value, converts the operating current value and the line voltage value into a digital signal, and obtains a quantized electric signal sequence;

[0045] The energy consumption value calculation sub-module extracts the voltage effective value and the current effective value under the same timestamp according to the quantized electric signal sequence, obtains the real-time power value by performing operation, performs integral accumulation operation on the multiple real-time power values in the preset unit time, and obtains the energy consumption value, thereby obtaining the instantaneous electric energy parameter set;

[0046] The time sequence data integration sub-module arranges the real-time power value, the energy consumption value, the device identifier and the acquisition timestamp as independent fields according to the time sequence, constructs a two-dimensional data table with a time sequence structure, and obtains the device energy consumption time sequence data stream.

[0047] Specifically, the continuous analog signal obtained by the current transformer in series in the production line motor power supply circuit and the voltage sensor in parallel on the line is discretely sampled by setting a fixed sampling frequency The sampling frequency is set according to the Nyquist sampling theorem, and its value is set to more than twice the highest order (for example, 50th harmonic, that is, 2.5 kHz) of the motor power frequency harmonic to ensure that the signal is not distorted , so as to determine the sampling time interval as 0.2 milliseconds, then the amplitude of the current and voltage analog signals collected at each sampling time point is quantized by a 16-bit analog-to-digital converter (ADC) , that is, 65536 discrete level values, and the quantization step is calculated as , wherein is 5V, is-5V, and N is 16 bits , the quantization step calculated is about 0.153mV, and the analog voltage amplitude of each sampling point is converted to the closest digital quantization value , and the conversion process is , this process is applied to the sampling of current and voltage signals at the same time, and a synchronous clock signal is used to ensure that the sampling actions of current and voltage are triggered at the same time point, avoiding calculation errors caused by phase difference, and finally converting the continuous analog electric signal into a quantized electric signal sequence composed of a series of discrete time and quantized amplitude digital values.

[0048] According to the quantized electric signal sequence obtained in the previous step, which contains the voltage and current discrete numerical points collected synchronously, first, in order to calculate the effective electric energy parameters, a calculation window is set, and the width of the window is set to be a complete power frequency alternating current period, for example, for 50Hz power frequency, the window width is 20ms, and under the sampling frequency of 5kHz, each window contains 100 sampling points, then, in each calculation window, the voltage effective value and the current effective value at the same timestamp are extracted, and the specific calculation method is that for N voltage sampling values and current sampling values in the window , the instantaneous power is calculated point by point, and then all the instantaneous power values in the window are averaged to obtain the average power of the window, that is, the real-time power value , and the calculation formula is , wherein N is the number of sampling points in the window, that is, 100, and the real-time power value represents the average electric energy consumption rate in 20ms, which is a very short time. Subsequently, a unit time for energy consumption accumulation is set, for example, 1 minute, and in the unit time, 3000 real-time power values (1 minute / 20ms) will be generated, and the 3000 real-time power values are integrated and accumulated to obtain the total energy consumption value in 1 minute , and the calculation method is , wherein T is the total number of calculation windows in the unit time, that is, 3000, , and T is the length of the calculation window, that is, 0.02s. Through such calculation, the original electric signal sequence is converted into real-time power values and energy consumption values containing physical meaning, which together constitute the instantaneous electric energy parameter set.

[0049] According to the instantaneous electric energy parameter set generated in the last step, which contains the real-time power value and energy consumption value calculated every unit of time (for example, 1 minute), the time series data integration process is started. First, a globally unique device identifier is assigned to each monitored device, which can be a string of codes such as "CNC-001-Cutting", ensuring the uniqueness of the data source. Then, a high-precision collection timestamp is obtained from a system clock synchronized with the Network Time Protocol (NTP) server every time the instantaneous electric energy parameter set is generated. The timestamp format follows the ISO 8601 standard, such as "2023-11-20T14:30:00.000Z", accurate to milliseconds. Then, the real-time power value, energy consumption value, device identifier, and collection timestamp are organized as independent fields into a new data record. Subsequently, the newly generated data record is added as a new row to a two-dimensional table structure data collection. This two-dimensional data table logically stores data snapshots at each time point by row and different data dimensions by column. Each row represents the energy consumption status of a specific device at a specific time point, and all rows are arranged in ascending order of timestamp, thereby constructing a two-dimensional data table with strict time series structure. New data records are continuously appended to the end of this table, forming a device energy consumption time series data stream.

[0050] The energy consumption feature analysis module includes:

[0051] The no-load period determination submodule sets the lower limit threshold of the running power of the air conditioning system according to the device energy consumption time series data stream, traverses the real-time power value in the device energy consumption time series data stream point by point, and marks the time range corresponding to the adjacent data points that continuously satisfy the condition of being lower than the lower limit threshold of the running power as the no-load period, and establishes the device no-load period data.

[0052] The energy consumption fluctuation calculation submodule retrieves the start timestamp and end timestamp of the no-load period according to the device no-load period data, calculates the difference between the two to obtain the duration, and then counts the number of occurrences of the no-load period in the preset unit period to obtain the occurrence frequency, and obtains the no-load feature statistical value.

[0053] The waste link calibration submodule extracts the energy consumption value corresponding to the current time period from the historical database according to the no-load feature statistical value, calculates the difference between the historical same period energy consumption value and the current energy consumption value, and then divides the energy consumption fluctuation rate by the historical same period energy consumption value, and integrates the duration, occurrence frequency, and energy consumption fluctuation rate to establish the energy consumption waste link identification set.

[0054] Specifically, the running state of the air conditioning system is analyzed by using the constructed device energy consumption time series data flow. First, a running power lower limit threshold for judging whether it is in an idle state is set. The threshold is not a fixed empirical value, but is dynamically generated through statistical analysis of historical data. The specific method is to retrieve the device energy consumption time series data flow of the past month, eliminate the data points with real-time power values lower than 100W (device standby or off state), sort the remaining valid running power data, and take the 5th percentile as the running power lower limit threshold. For example, after analyzing tens of thousands of running power data points, it is found that 95% of the values are higher than 850W, so 850W is set as the running power lower limit threshold. The threshold is recalculated every month to adapt to device aging or seasonal changes. After setting the threshold, the real-time power values in the latest device energy consumption time series data flow are traversed point by point. When the first data point with a real-time power value lower than 850W is detected, the corresponding timestamp is recorded as the start time of the idle period. Then continue to traverse forward until the first data point with a real-time power value greater than or equal to 850W is found. The timestamp of the previous data point is recorded as the end time of the idle period. This pair of start and end timestamps is stored as a complete idle event. Repeat this process to mark all adjacent data points corresponding to the time range that meets the condition of being lower than the running power lower limit threshold. The time ranges are aggregated to establish structured device idle period data.

[0055] Based on the established device idle period data composed of a series of start and end timestamp pairs, the idle feature is quantitatively analyzed. First, traverse each idle period record to calculate the duration of the idle event by calculating the difference between the end timestamp and the start timestamp. For example, if the start time is "14:30:15" and the end time is "14:35:45", the duration is 330 seconds. Perform this calculation for all idle events to obtain a list of durations. Next, set a preset unit period for frequency statistics, which is set according to the production plan, such as a standard 8-hour work shift from 8am to 4pm. Then, count the total number of independent idle periods that occur within this 8-hour unit period. This count is the frequency of idle occurrence. For example, within the period from 8am to 4pm, 5 idle events are recorded, so the frequency is 5. Finally, the durations of all idle events within the unit period are added up to obtain the total idle duration. The total idle duration and the frequency of occurrence are associated with the corresponding time period (such as date and shift) and integrated together to obtain the idle feature statistics.

[0056] According to the obtained no-load characteristic statistical value, the specific energy consumption waste link is calibrated. First, the historical same period energy consumption value corresponding to the current analysis period is extracted from the historical database. The "same period" here is defined as the period within the past three months that is the same as the current week and time period. For example, if the current analysis is from 10:00 to 11:00 on Tuesday, the energy consumption values of the same period in the past 12 Tuesdays are extracted, and the arithmetic mean value is calculated As a benchmark for historical same period energy consumption value, the actual total energy consumption value of the current period is then calculated And the energy consumption fluctuation rate is calculated using the following energy consumption fluctuation rate formula which introduces the no-load characteristic adjustment :

[0057] ;

[0058] Wherein, is the total energy consumption value of the current period, is the average total energy consumption value of the historical same period, is a small positive number set to prevent the denominator from being zero, and the value is 10⁻ 6 , is the total no-load duration in the current period obtained from the no-load characteristic statistical value, is the total duration of the current analysis period (e.g. 1 hour, i.e. 3600 seconds), is the no-load occurrence frequency obtained from the no-load characteristic statistical value, is the no-load impact adjustment coefficient, which is a dimensionless empirical constant set according to the characteristics of the device. For example, for an air conditioning system with fast response speed, the waste caused by frequent start-stop is more significant, and it can be set to 0.8. This formula amplifies the energy consumption fluctuation caused by unreasonable start-stop by using the logarithmic term of the no-load duration ratio and the occurrence frequency based on the calculation of traditional energy consumption deviation. Finally, the duration of each no-load period, the occurrence frequency in the unit period, and the energy consumption fluctuation rate These three indicators are integrated to form a structured record, and multiple such records collectively establish the energy consumption waste link identification set.

[0059] The device control strategy module includes:

[0060] The motor time period optimization submodule retrieves the preset time-of-use electricity price interval according to the energy consumption waste link identification set, calculates the overlap degree between the identified no-load period and the electricity price peak period, and schedules the non-production task with the highest overlap degree to the electricity price valley period for execution to form a motor optimal operation schedule table;

[0061] The variable frequency parameter adjustment submodule reversely calculates a target frequency value negatively correlated with the energy fluctuation rate according to the energy waste link identification set and a preset reference frequency value, sets an operation start-stop time point of the air conditioning system, and generates an air conditioning system control parameter;

[0062] The instruction set generation submodule converts the start and end time points of the preferred operation period into binary start-stop control bits, encodes the target frequency value and the operation start-stop time point into byte stream data, and generates a device operation optimization instruction set according to the motor preferred operation schedule and the air conditioning system control parameter.

[0063] Specifically, according to the energy waste link identification set established in the preceding step, a preset time-of-use electricity price interval definition table is first retrieved from a local configuration library. The table explicitly divides the electricity price period within 24 hours of a day, for example, the peak period is 10:00-12:00 and 18:00-20:00, the electricity price is 1.2 yuan / degree, the flat period is 08:00-10:00, 12:00-18:00 and 20:00-22:00, the electricity price is 0.8 yuan / degree, and the valley period is 22:00 to 08:00 the next day, the electricity price is 0.4 yuan / degree. Then, each idle period record in the energy waste link identification set is traversed, and the start and end time stamps are extracted. Meanwhile, schedulable tasks are filtered from a predefined non-production task list, such as device preheating, data backup, internal cleaning cycle, etc. These tasks have flexibility in time. The overlap degree of each identified idle period and each schedulable non-production task is calculated. The calculation formula of the overlap degree is as follows:

[0064] ;

[0065] Among them, represents the time interval of the identified idle period, represents the time interval of the electricity price peak period, the function calculates the intersection of the two time intervals, the function calculates the length of the time interval, calculates the overlap degree of all idle periods and the electricity price peak period, and identifies the combination with the highest overlap degree. For example, a 30-minute idle period from 10:30 to 11:00 completely overlaps with the peak period of 10:00-12:00, and the overlap degree is 100%. Then, the non-production task with a duration of less than 30 minutes originally planned to be executed in this period, such as device data automatic uploading, is rescheduled to the electricity price lowest valley period (for example, 02:00 in the morning) for execution. The adjusted task name, original execution time, new execution time, associated device identifier, etc. are recorded one by one. Finally, all adjustment items are summarized to form a motor preferred operation schedule.

[0066] According to the energy waste link identification set of energy fluctuation rate value, the frequency converter operation parameters of air conditioning system are dynamically adjusted. First, a preset reference frequency value of air conditioning system is set . The setting of the reference value is not fixed, but is obtained by analyzing the historical operation frequency data of air conditioning system under similar external temperature and internal load conditions in the past month, taking 75 percentile of the operation frequency, for example, in the historical data, the frequency of air conditioning compressor is lower than 45 Hz for 75% of the time, then 45 Hz is set . This provides a stable reference for adjustment, then, according to the energy waste link identification set of energy fluctuation rate value, the target frequency value negatively related to energy fluctuation rate is calculated by a negative feedback regulation model , the calculation formula is as follows:

[0067] ;

[0068] Among them, is the calculated target frequency value, is the preset reference frequency value (for example, 45 Hz), is the energy fluctuation rate value obtained from the energy waste link identification set, is the regulation sensitivity coefficient, which is a dimensionless parameter, used to control the degree of frequency change with fluctuation rate, according to experience and equipment characteristics test, for large space not sensitive to temperature change, the value can be set to 3.0, for small space which needs fast response, it can be set to 5.0, is the reference center point of energy fluctuation rate, usually set to 0.1, representing that 10% energy fluctuation is an acceptable range, this formula uses the smoothing characteristics of Sigmoid function, when is much larger than , will tend to a lower value, otherwise it will tend to , at the same time, combined with the information of no-load period in the energy waste link identification set, the starting time point of no-load period is moved forward by a preset buffer time (for example, 5 minutes) as the stop time point of air conditioning system, the ending time point of no-load period is also moved forward by 5 minutes as the next start time point, finally, the calculated target frequency value and the set operation start-stop time point are combined to generate air conditioning system control parameters.

[0069] According to the motor optimal operation schedule and air conditioning system control parameters generated in the previous steps, unified instruction coding and integration are performed. First, the motor optimal operation schedule is processed. Each scheduling record in the table is traversed to extract the associated device identifier and the start and end time points of the optimal operation period. These time points are compared with the current system time. When the system time reaches the start time point of a task, a binary start-stop control bit is generated, which is set to "1" (representing start). When the end time point is reached, a "0" (representing stop) control bit is generated. Next, the air conditioning system control parameters are processed. The target frequency value and the operation start-stop time point are extracted. The floating-point target frequency value (e.g., 42.5 Hz) is multiplied by a scaling factor (e.g., 10) and rounded to an integer (425). This integer is then converted to a 16-bit binary number. The operation start-stop time point is converted to the total number of seconds since midnight. Then, all the control information obtained through the above processing is packaged according to a predefined communication protocol format. This format includes a frame header (2 bytes, fixed as 0xAA55), a device address (4 bytes), a command type (1 byte, e.g., 0x01 for motor start-stop and 0x02 for frequency adjustment), a data length (1 byte), and data payload. For motor start-stop instructions, the data payload is a 1-byte binary start-stop control bit. For frequency adjustment instructions, the data payload is a byte stream composed of the target frequency value and the operation start-stop time point. Finally, a CRC16 checksum (2 bytes) is appended to the end of the data packet. All device control instruction packets are combined to generate a device operation optimization instruction set.

[0070] The remote instruction execution module includes:

[0071] The instruction parsing and sending submodule parses the device address field in the device operation optimization instruction set to locate the intelligent relay and sends the binary switch state bit. It also sends the encoded target frequency value byte stream to the frequency controller to obtain the device control electrical signal.

[0072] The real-time energy consumption monitoring submodule continuously extracts real-time power values from the device energy consumption time series data stream and matches the corresponding time-of-use period preset power safety threshold based on the current timestamp. It compares the two values to obtain a power overrun determination result.

[0073] The abnormal alarm generation submodule locks the current device identifier, timestamp, and preset abnormal type code when the determination result is a Boolean true value, and combines these three data into a fixed format data packet to obtain a device abnormal state alarm signal.

[0074] Specifically, according to the generated device operation optimization instruction set, the specific instruction analysis and delivery operation is executed, first, the data packets in the instruction set are analyzed one by one, the device address field of each data packet is read, the field is a 4-byte unique identifier, for example, "0x000100A1", then, a pre-established device address-communication port mapping table is searched, the table maps each logical device address to a physical communication interface and address, for example, "0x000100A1" is mapped to "RS485-COM3-Address5", so as to locate the target device, such as the intelligent relay for controlling the motor of the production line or the frequency converter for controlling the air conditioning system, after locating, the command type field in the instruction packet is processed according to the branching, if the command type is motor start-stop, the single binary switch state bit ("1" or "0") in the data load is sent to the specified intelligent relay through the corresponding RS485 interface according to the Modbus RTU protocol Write Single Coil (function code 0x05) function, if the command type is frequency adjustment, the encoded target frequency value byte stream and start-stop time byte stream in the data load are also sent to the corresponding register address through the corresponding communication interface according to the communication protocol supported by the frequency converter (for example, USS protocol), for example, the frequency setting value is written to P1082 parameter address, the whole sending process does not involve secondary transformation or filtering of the instruction content, the encoded byte stream is directly converted into physical layer electrical signal to obtain device control electrical signal.

[0075] After the instruction is delivered, the latest real-time power value is extracted from the device energy consumption time series data stream obtained from the energy data regularization module, and real-time safety monitoring is performed, first, a time-division power safety threshold table is established, the establishment of the table is based on statistical analysis of the energy consumption data of the same time period (for example, all Mondays from 9:00 to 10:00) of the device in the past month, the specific method is to calculate the arithmetic mean and standard deviation of all real-time power values in the period, then the power safety threshold is set to the average value plus 2.5 times the standard deviation, for example, if the historical average power of a device from 9:00 to 10:00 on Monday is 5kW, and the standard deviation is 0.4kW, then the power safety threshold of this period is set to The threshold table covers all working hours in a day and is updated automatically once a month. During monitoring, according to the current system timestamp, for example, "10:45:30", the threshold corresponding to the current time point is immediately queried and matched from the power safety threshold table, for example, the threshold of the 10:00-11:00 period is 6.2kW. Then, the latest real-time power value (for example, 6.5kW) extracted from the device energy consumption time series data stream is compared with the queried threshold (6.2kW) in numerical size. If the real-time power value is greater than the corresponding sub-period preset power safety threshold, the comparison result is a Boolean true value, otherwise it is a Boolean false value, and the power overrun determination result is obtained.

[0076] According to the power overrun determination result generated in the previous step, subsequent abnormal state processing and alarm signal generation are performed. When the value of the determination result is a Boolean true value, the alarm generation process is triggered immediately. First, three key information leading to this overrun event is locked, which is the unique identifier of the current device (for example, "CNC-001-Cutting"), the accurate timestamp triggering the overrun determination (for example, "2023-11-20T10:45:32.500Z"), and a preset abnormal type code, which is defined in an abnormal type reference table. For example, 0x0001 represents "power upper limit", 0x0002 represents "current surge", and 0x0003 represents "voltage drop". In this scenario, the abnormal type code is locked as 0x0001. Then, the three data (device identifier, timestamp, abnormal type code) are combined into a fixed format data packet, which is structured and packaged in JSON format. The format example is as follows: {"deviceId": "CNC-001-Cutting", "timestamp": "2023-11-20T10:45:32.500Z", "errorCode": "0x0001", "errorMsg": "Power consumption exceeds threshold"}. This data packet does not contain any original power value or threshold information, but only contains the core elements of the event. The JSON object is serialized into a string to obtain the device abnormal state alarm signal.

Claims

1. A cloud-based energy management and control system, characterized in that, The system includes: The energy data normalization module uses a series current transformer to collect the operating current value of the production line motor and a parallel voltage sensor to monitor the line voltage value. It converts the operating current value and line voltage value into digital signals by periodically sampling and quantizing the continuous analog signals. Then, it calculates the real-time power value by the effective voltage value and the effective current value at the same time stamp. Finally, it accumulates the real-time power value within a unit time to obtain the energy consumption value. By combining the real-time power value, energy consumption value, equipment identifier and collection timestamp, it obtains the equipment energy consumption time-series data stream. The energy consumption characteristic analysis module is used to set a lower limit threshold for the operating power of the air conditioning system based on the energy consumption time-series data stream of the equipment, determine the continuous time period in the energy consumption time-series data stream where the real-time power value is lower than the lower limit threshold for the operating power as an idle period, record the start and end timestamps of the idle period, calculate the time difference to obtain the duration, count the number of times the idle period occurs within a unit period to obtain the occurrence frequency, compare the energy consumption value of the same period in history with the current energy consumption value, calculate the difference between the energy consumption value of the same period in history and the current energy consumption value, and the ratio of the energy consumption value of the same period in history to obtain the energy consumption fluctuation rate, and establish an identification set of energy waste links.

2. The cloud-based energy management and control system according to claim 1, characterized in that, The system also includes: The equipment control strategy module is used to retrieve a preset time-of-use electricity price range based on the energy waste identification set, match the identified idle periods with the peak electricity price periods, migrate non-production tasks to the low electricity price periods, form the preferred operating period for the production line motor, and adjust the reference frequency of the air conditioning system inverter in reverse according to the energy consumption fluctuation rate value in the energy waste identification set, calculate the target frequency value, set the start and stop time points, and generate a set of equipment operation optimization instructions. The remote command execution module is used to send start / stop commands, including device address and switch status bits, to the intelligent relay according to the device operation optimization command set, and to send frequency adjustment commands, including target frequency values, to the frequency converter. At the same time, it continuously extracts the real-time power value from the device energy consumption time-series data stream and compares it with the preset power safety threshold for different time periods. When the real-time power value exceeds the power safety threshold, it generates structured data including device identifier, abnormality type, and timestamp to obtain an alarm signal for abnormal device status.

3. The cloud-based energy management and control system according to claim 1, characterized in that, The energy data normalization module includes: The electrical signal digitization submodule uses a series current transformer to collect the operating current value of the production line motor and a parallel voltage sensor to monitor the line voltage value. By setting a fixed sampling frequency, the continuous analog signal is discretized and sampled. Then, the amplitude of each sampling point is mapped to a preset discrete level value, and the operating current value and line voltage value are converted into digital signals to obtain a quantized electrical signal sequence. The energy consumption numerical calculation submodule extracts the effective voltage and effective current values ​​at the same timestamp based on the quantized electrical signal sequence and performs calculations to obtain the real-time power value. It then performs integration and accumulation calculations on multiple real-time power values ​​within a preset unit time to obtain the energy consumption value and a set of instantaneous electrical energy parameters. The time-series data integration submodule, based on the instantaneous power parameter set, arranges the real-time power value, energy consumption value, device identifier, and acquisition timestamp as independent fields in chronological order to construct a two-dimensional data table with a time-series structure, and obtains the device energy consumption time-series data stream.

4. The cloud-based energy management and control system according to claim 1, characterized in that, The energy consumption characteristic analysis module includes: The idle period determination submodule sets the lower limit threshold of the air conditioning system operating power based on the equipment energy consumption time series data stream, iterates through the real-time power values ​​in the equipment energy consumption time series data stream point by point, and marks the time range corresponding to adjacent data points that continuously meet the condition of being lower than the lower limit threshold of operating power as idle periods, thus establishing equipment idle period data. The energy consumption fluctuation calculation submodule retrieves the start and end timestamps of the idle period based on the equipment idle period data, calculates the difference between the two to obtain the duration, and then counts the number of times the idle period occurs within a preset unit period to obtain the occurrence frequency, thereby obtaining the idle characteristic statistical value. The waste process identification submodule extracts the energy consumption value corresponding to the current time period from the historical database based on the statistical value of the no-load characteristics, calculates the difference between the energy consumption value of the same period in history and the current energy consumption value, divides it by the energy consumption value of the same period in history to obtain the energy consumption fluctuation rate, and integrates the duration, frequency of occurrence and energy consumption fluctuation rate to establish an energy waste process identification set.

5. The cloud-based energy management and control system according to claim 2, characterized in that, The device control strategy module includes: The motor time-of-use optimization submodule retrieves the preset time-of-use electricity price range based on the energy waste identification set, calculates the overlap between the identified idle time periods and peak electricity price periods, and schedules the non-production tasks with the highest overlap to be executed during the low electricity price period, thus forming a motor optimization operation scheduling table. The variable frequency parameter adjustment submodule identifies the energy consumption fluctuation rate value in the energy waste process, performs a linear combination calculation with the preset reference frequency value, calculates the target frequency value that is negatively correlated with the energy consumption fluctuation rate, sets the start and stop time points of the air conditioning system, and generates the air conditioning system control parameters.

6. The cloud-based energy management and control system according to claim 5, characterized in that, The device control strategy module also includes: The instruction set generation submodule converts the start and end times of the preferred running segment into binary start and stop control bits based on the motor optimal running schedule table and air conditioning system control parameters. At the same time, it encodes the target frequency value and the start and stop times into byte stream data to generate the equipment operation optimization instruction set.

7. The cloud-based energy management and control system according to claim 2, characterized in that, The remote instruction execution module includes: The instruction parsing and sending submodule parses the device address field in the instruction set to locate the intelligent relay based on the device operation optimization instruction set, sends the binary switch status bit, and sends the encoded target frequency value byte stream to the frequency converter to obtain the device control electrical signal. The real-time energy consumption monitoring submodule continuously extracts the real-time power value from the device's energy consumption time-series data stream, matches the corresponding time-segmented preset power safety threshold based on the current timestamp, compares the two values, and obtains the power over-limit judgment result.

8. The cloud-based energy management and control system according to claim 7, characterized in that, The remote instruction execution module further includes: The abnormal alarm generation submodule, based on the power over-limit judgment result, immediately locks the current device identifier, timestamp, and preset abnormal type code when the judgment result is a Boolean true value, and combines these three data items into a data packet with a fixed format to obtain the device abnormal status alarm signal.

9. The energy management and control method of the cloud platform-based energy management and control system according to any one of claims 1-8, characterized in that, Includes the following steps: The system collects the operating current values ​​of the production line motors and monitors the line voltage values. It converts the operating current and line voltage values ​​into digital signals by periodically sampling and quantizing the continuous analog signals. Then, it calculates the real-time power value by analyzing the effective voltage and current values ​​at the same timestamp. Finally, it accumulates the real-time power values ​​within a unit of time to obtain the energy consumption value. By combining the real-time power value, energy consumption value, equipment identifier, and collection timestamp, it obtains the equipment energy consumption time-series data stream. Based on the energy consumption time-series data stream of the equipment, a lower limit threshold for the operating power of the air conditioning system is set. Continuous time periods in the energy consumption time-series data stream where the real-time power value is lower than the lower limit threshold are determined as idle periods. The start and end timestamps of the idle periods are recorded, the time difference is calculated to obtain the duration, the number of idle periods occurring within a unit period is counted to obtain the frequency of occurrence, the energy consumption value is compared with the historical energy consumption value for the same period and the current energy consumption value, the difference between the historical energy consumption value for the same period and the current energy consumption value, and the ratio of the historical energy consumption value for the same period are calculated to obtain the energy consumption fluctuation rate, thus establishing an identification set for energy waste links. Based on the energy waste identification set, a preset time-of-use electricity price range is retrieved, the identified idle periods are matched with peak electricity price periods, non-production tasks are moved to low electricity price periods, forming the preferred operating period for the production line motors, and the reference frequency of the air conditioning system inverter is adjusted in reverse according to the energy consumption fluctuation rate value in the energy waste identification set, the target frequency value is calculated, the start and stop time points are set, and a set of equipment operation optimization instructions is generated. According to the equipment operation optimization instruction set, start / stop instructions including equipment address and switch status bits are sent to the intelligent relay, and frequency adjustment instructions including target frequency value are sent to the frequency converter. At the same time, the real-time power value in the equipment energy consumption time-series data stream is continuously extracted and compared with the power safety threshold preset in different time periods. When the real-time power value exceeds the power safety threshold, structured data including equipment identifier, abnormality type and timestamp is generated to obtain the equipment abnormality alarm signal.

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