Energy-saving control method and system of electric energy meter

Abstract

The application relates to the technical field of electric energy meter control, and discloses an energy-saving control method and system for an electric energy meter, which comprises the following steps: obtaining a preliminary electricity consumption behavior profile containing a historical electricity consumption peak value and a seasonal time period; combining environmental data and change data in a preset environmental database to construct an electricity consumption behavior and environment correlation mapping table; searching for the historical electricity consumption peak value and calculating a deviation value according to current environmental data and real-time electricity consumption load; if the deviation exceeds a preset threshold value and falls into a fluctuation interval, a preliminary energy-saving control parameter set is generated; scene adaptation is carried out in combination with a temperature fluctuation value and a humidity change index to obtain a temporary parameter set; the temporary parameter set is secondarily calibrated according to a device operation state and a performance constraint to generate an optimized parameter set; and finally, an energy-saving control instruction is generated in combination with illumination intensity and the seasonal time period, and an execution sequence is determined. The application realizes accurate and adaptive energy-saving control while meeting the current environmental needs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy meter control, and in particular to an energy-saving control method and system for electric energy meter. BACKGROUND

[0002] In the field of energy management, efficient use of electric energy and energy-saving control are considered as important pillars to achieve sustainable development, which can significantly reduce energy consumption and environmental burden. With the continuous growth of social demand for electricity, how to improve the efficiency of electricity use through technical means, optimize the operation of the power grid, and achieve energy saving and emission reduction while ensuring the user's electricity experience has become a key issue that needs to be addressed.

[0003] In one prior art, the energy-saving system relies on fixed threshold settings or predefined control logic, such as cutting off part of the non-critical load when the load exceeds a certain set value; or uniformly reducing device power during a specific time period, the system uses centralized demand response to issue load reduction instructions from the grid side.

[0004] However, in the existing system, only relying on set values or uniformly reducing device power during a specific time period, the control is relatively simple, and lacks the ability to control large-scale multi-user, multi-scenario and personalized control, resulting in difficulty in meeting user demand in terms of adaptability and accuracy. SUMMARY

[0005] The present application provides an energy-saving control method and system for electric energy meter to realize real-time energy-saving control for multi-user, multi-device and multi-scenario.

[0006] In a first aspect, to solve the above technical problems, the present application provides an energy-saving control method for electric energy meter, comprising:

[0007] obtaining a preliminary electricity consumption behavior profile containing historical electricity consumption peaks and seasonal time periods;

[0008] constructing according to the preliminary electricity consumption behavior profile and the environmental data recorded in the preset environmental database to obtain an electricity consumption behavior and environment association mapping table; wherein the environmental data includes temperature fluctuation value, humidity change index and light intensity;

[0009] obtaining current environmental data and current electricity consumption load at the current time, finding the corresponding historical electricity consumption peak in the electricity consumption behavior and environment association mapping table, and calculating the deviation value of the current electricity consumption load and the historical electricity consumption peak;

[0010] when it is determined that the deviation value exceeds the preset deviation threshold and falls within the preset fluctuation interval, and according to the pre-obtained historical control profile, a preliminary energy-saving control parameter set containing behavior adjustment signal and adjustment amplitude is generated;

[0011] According to the temperature fluctuation value and the humidity change index, scene adaptation adjustment is performed on the preliminary energy-saving control parameter set to obtain a temporary energy-saving control parameter set;

[0012] Real-time operation state data of the electrical equipment is acquired, and is matched with preset equipment performance constraint conditions, if the equipment performance constraint conditions are not met, the temporary energy-saving control parameter set is calibrated again according to the adjustment amplitude and the fluctuation interval to obtain an optimized energy-saving control parameter set;

[0013] According to the optimized energy-saving control parameter set, an energy-saving control instruction is generated in combination with the light intensity and the seasonal time period, and an execution order of the energy-saving control instruction is determined.

[0014] Preferably, the preliminary electrical behavior archive containing historical electrical peak value and seasonal time period is acquired, including:

[0015] Multi-dimensional electrical information is extracted from a preset electrical archive, and is classified according to the electrical behavior mode of different users in a specific scene to obtain classified multi-dimensional electrical information;

[0016] According to the classified multi-dimensional electrical information, a preset electrical feature database is used for comparison to obtain the preliminary electrical behavior archive containing historical electrical peak value and seasonal time period.

[0017] Preferably, the environment data is recorded according to the preliminary electrical behavior archive and a preset environment database, and a construction operation is performed to obtain an electrical behavior and environment association mapping table, including:

[0018] According to the preliminary electrical behavior archive, the electrical behavior is matched with the environment data in combination with the temperature fluctuation value, the humidity change index and the light intensity to obtain an initial data set containing the environment data and the electrical behavior;

[0019] According to the initial data set and the seasonal time period, seasonal fluctuation and behavior mode are classified, key features related to the environment data are acquired, and an intermediate mapping table containing the key features is obtained;

[0020] According to the intermediate mapping table, the environment data is integrated to obtain a final electrical behavior and environment association mapping table.

[0021] Preferably, the preliminary energy-saving control parameter set containing behavior adjustment signal and adjustment amplitude is generated according to the historical control archive, including: distribution data of the fluctuation interval is obtained by comparison according to the fluctuation interval;

[0022] If the distribution data meets a preset trigger condition, a corresponding behavior adjustment signal is generated;

[0023] According to the behavior adjustment signal, the environmental data and the fluctuation interval, the historical control profile is matched to obtain an initial adjustment amplitude;

[0024] According to the power consumption behavior in the preliminary power consumption behavior profile, the initial adjustment amplitude is revised within the fluctuation range of the power consumption behavior to obtain a final adjustment amplitude.

[0025] Preferably, according to the temperature fluctuation value and the humidity change index, the preliminary energy-saving control parameter set is adjusted to obtain a temporary energy-saving control parameter set, including:

[0026] When the temperature fluctuation value exceeds a preset temperature fluctuation range or the humidity change index exceeds a preset humidity fluctuation range, the preliminary energy-saving control parameter set is adjusted to obtain a temporary parameter set;

[0027] According to the temporary parameter set, a matching target power consumption behavior is extracted from the historical control profile, and if the target power consumption behavior does not meet a preset scene requirement, the temporary parameter set is optimized according to the target power consumption behavior to obtain a temporary energy-saving control parameter set.

[0028] Preferably, according to the adjustment amplitude and the fluctuation interval, the temporary energy-saving control parameter set is secondarily calibrated to obtain an optimized energy-saving control parameter set, including:

[0029] If the fluctuation interval exceeds a preset device performance constraint condition range, the temporary energy-saving control parameter set is dynamically adjusted based on the adjustment amplitude and the environmental data to determine a calibrated parameter set;

[0030] According to the calibrated parameter set, a matching target operation mode is obtained from the historical control profile, and if the target operation mode meets a preset operation mode requirement, the optimized energy-saving control parameter set is determined according to the calibrated parameter set.

[0031] Preferably, according to the optimized energy-saving control parameter set, a energy-saving control instruction is generated in combination with the light intensity and the seasonal time period, including:

[0032] According to the seasonal time period, a matching light threshold value range is selected from a preset seasonal light threshold value library;

[0033] If the light intensity exceeds the light threshold value range, the optimized energy-saving control parameter set is adjusted to obtain a preliminary environmental adaptation scheme;

[0034] A current weather mode category is obtained;

[0035] According to the real-time power equipment, the device type of the real-time power equipment is obtained in combination with preset classification data of different devices;

[0036] The historical power peak of the device type is matched from the preliminary power consumption behavior archive according to the device type and the seasonal time period;

[0037] The current device type power load is obtained, and a load deviation value of the historical power peak and the current device type power load is calculated;

[0038] According to the preliminary environment adaptation scheme, the load deviation value and the current weather mode category, a sequence of issuance is determined according to a preset priority rule, and a classified energy-saving control instruction priority set is obtained;

[0039] According to the classified energy-saving control instruction priority set and a preset energy-saving target of the device type, parameter matching is performed, and an energy-saving control instruction is obtained.

[0040] Preferably, after the execution sequence of the energy-saving control instruction is determined, the method further comprises:

[0041] According to the execution sequence of the energy-saving control instruction, the energy-saving control instruction is issued in priority order;

[0042] It is judged whether there is an abnormal feedback, and if there is, the execution sequence of the energy-saving control instruction is adjusted to obtain a final issuance execution result.

[0043] In a second aspect, the present application provides an energy-saving control system of an electric energy meter, characterized in that it comprises:

[0044] A data acquisition module acquires a preliminary power consumption behavior archive containing historical power peaks and seasonal time periods;

[0045] A mapping construction module performs construction operation according to the preliminary power consumption behavior archive and environmental data recorded in a preset environment database to obtain a power consumption behavior and environment association mapping table; wherein the environmental data includes temperature fluctuation value, humidity change index and illumination intensity;

[0046] A difference calculation module obtains current environmental data and current power load at the current time, finds corresponding historical power peaks in the power consumption behavior and environment association mapping table, and calculates a deviation value of the current power load and the historical power peaks;

[0047] A parameter acquisition module generates a preliminary energy-saving control parameter set containing behavior adjustment signal and adjustment amplitude according to a historical control archive pre-acquired when it is determined that the deviation value exceeds a preset deviation threshold and falls into a preset fluctuation interval;

[0048] The parameter adjustment module performs scene adaptation adjustment on the preliminary energy-saving control parameter set according to the temperature fluctuation value and the humidity change index, to obtain a temporary energy-saving control parameter set.

[0049] The secondary calibration module obtains the running state data of the real-time power consumption equipment, and performs secondary calibration on the temporary energy-saving control parameter set according to the adjustment range and the fluctuation interval, to obtain an optimized energy-saving control parameter set, if the device performance constraint condition is not met.

[0050] The instruction generation module generates an energy-saving control instruction according to the optimized energy-saving control parameter set, in combination with the illumination intensity and the seasonal time period, and determines the execution order of the energy-saving control instruction.

[0051] Compared with the prior art, the present application has the following beneficial effects:

[0052] (1) The present application can generate personalized energy-saving control strategies for each user according to the power consumption habits and behavior patterns of different users in different environments, by obtaining a preliminary power consumption behavior profile containing historical power consumption peaks and seasonal time periods, and constructing a power consumption behavior and environment correlation mapping table in combination with real-time collected environmental data including temperature fluctuation value, humidity change index and illumination intensity, thereby realizing personalized control of large-scale multi-user and multi-scene, and meeting the personalized energy-saving needs of users.

[0053] (2) On the basis of constructing the power consumption behavior and environment correlation mapping table, the present application can obtain real-time environmental data and current power consumption load, find the corresponding historical power consumption peak and calculate the deviation value, when the deviation value exceeds the preset deviation threshold and falls into the fluctuation interval, generate a preliminary energy-saving control parameter set according to the historical control profile, and perform scene adaptation adjustment on the preliminary energy-saving control parameter set according to the real-time temperature fluctuation value and humidity change index, to obtain a temporary energy-saving control parameter set. This process fully considers the changes in environmental data and the fluctuations in real-time power consumption load, and can adaptively adjust the energy-saving control strategy to better adapt to the actual power consumption situation, thereby improving the adaptability of energy-saving control.

[0054] (3) After obtaining the temporary energy-saving control parameter set, the running state data of the real-time power consumption equipment is further acquired, and is matched with the preset equipment performance constraint condition, if the equipment performance constraint condition is not met, the temporary energy-saving control parameter set is calibrated again according to the adjustment range and the fluctuation interval, the optimized energy-saving control parameter set is obtained, finally, the energy-saving control instruction is generated combined with the illumination intensity and the seasonal time period, and the execution order is determined. Through multi-level data analysis and calibration, the energy-saving control parameters and instructions are accurately determined, so that the energy-saving control measures can be accurately applied to the actual power consumption scene, precise energy-saving control is realized, and the energy-saving effect is improved.

[0055] (4) When constructing the power consumption behavior and environment correlation mapping table, the environmental data such as temperature fluctuation value, humidity change index and illumination intensity are included, and in the subsequent energy-saving control parameter adjustment process, the influence of these environmental factors is also fully considered, such as scene adaptive adjustment of the preliminary energy-saving control parameter set according to the temperature fluctuation value and the humidity change index, and generation of the energy-saving control instruction combined with the illumination intensity and the seasonal time period, so that the energy-saving control strategy is more comprehensive and reasonable, and can better adapt to the energy-saving needs under different environmental conditions, and improve the comprehensive effect of energy-saving control. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 is the energy-saving control method flow diagram of the electric energy meter provided by the first embodiment of the present application;

[0057] Figure 2 is the energy-saving control system structure schematic diagram of the electric energy meter provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0059] With reference to Figure 1 The first embodiment of the present application provides an energy-saving control method of an electric energy meter, comprising the following steps:

[0060] S101, acquiring a preliminary power consumption behavior profile containing historical power consumption peak value and seasonal time period;

[0061] S102, constructing according to the preliminary power consumption behavior profile and the environmental data recorded in the preset environmental database record, obtaining a power consumption behavior and environment correlation mapping table; wherein the environmental data includes temperature fluctuation value, humidity change index and illumination intensity;

[0062] S103, obtaining current environment data and current power consumption load at the current time, finding the corresponding historical power consumption peak in the power consumption behavior and environment mapping table, and calculating the deviation value of the current power consumption load and the historical power consumption peak;

[0063] S104, when it is determined that the deviation value exceeds the preset deviation threshold and falls into the preset fluctuation interval, and according to the historical control profile obtained in advance, a preliminary energy-saving control parameter set containing behavior adjustment signal and adjustment amplitude is generated;

[0064] S105, according to the temperature fluctuation value and the humidity change index, the preliminary energy-saving control parameter set is scene-adapted and adjusted to obtain a temporary energy-saving control parameter set;

[0065] S106, obtaining the running state data of real-time power consumption equipment, and matching with the preset equipment performance constraint condition, if the equipment performance constraint condition is not met, then according to the adjustment amplitude and the fluctuation interval, the temporary energy-saving control parameter set is secondarily calibrated to obtain an optimized energy-saving control parameter set;

[0066] S107, according to the optimized energy-saving control parameter set, combining the light intensity and the seasonal time period to generate an energy-saving control instruction, and determining the execution order of the energy-saving control instruction.

[0067] In step S101, a preliminary power consumption behavior profile containing historical power consumption peak and seasonal time period needs to be obtained.

[0068] In an implementation mode, the preliminary power consumption behavior profile containing historical power consumption peak and seasonal time period includes steps S201 to S202:

[0069] S201, extract multi-dimensional power consumption information from the preset power consumption profile, and classify the power consumption behavior mode of different users in a specific scene to obtain classified multi-dimensional power consumption information.

[0070] It should be noted that the preset power consumption profile refers to a structured database constructed based on long-term collection, statistics and induction of user power consumption history data and behavior mode; the multi-dimensional power consumption information is extracted from the power consumption profile, including users, seasonal time periods, equipment and power consumption load; when classifying these information, a multi-level classification strategy is adopted: first, according to different users, the information is preliminarily divided according to the seasonal time period dimension, and then classified according to the user division, and finally refined to the equipment level and load type, such as lighting, electric heating, electric motor and different power consumption modes. This hierarchical classification method improves the manageability and interpretability of data.

[0071] It should be noted that in the process of extracting multi-dimensional power consumption information from the preset power consumption profile, if the power consumption load of an entry exceeds the preset threshold range, for example, the power consumption load suddenly increases by 3 times, and other data is normal, it indicates that there may be data anomalies, and the entry needs to be removed.

[0072] S202, according to the classified multi-dimensional power consumption information, a preset power consumption feature database is used for comparison, and the preliminary power consumption behavior profile containing historical power consumption peak and seasonal time period is obtained.

[0073] It should be noted that the preset power consumption feature database is a database classified according to typical power consumption behavior, containing device type, seasonal time period, peak load interval, and average peak fluctuation range. The power consumption feature database is the data source of the preliminary power consumption behavior profile, and provides important prior knowledge and structural support for subsequent power consumption behavior matching.

[0074] In an implementation mode, the multi-dimensional power consumption information of each category is compared with the preset power consumption feature database, the most matched item of the power consumption feature database is obtained, and all entries of the item are added to the preliminary power consumption behavior profile; wherein the peak load interval in the entry contains the statistical range information of the historical power consumption peak.

[0075] It should be noted that the comparison method can be any one of rule matching method, vector similarity matching method or machine learning clustering method. Exemplarily, using the vector similarity matching method, the multi-dimensional power consumption information is first encoded into a vector, and each item in the power consumption feature database also has a standard vector. By calculating the cosine similarity of the two vectors, the item with the maximum cosine similarity is found to match.

[0076] In step S102, according to the preliminary power consumption behavior profile and the environmental data recorded by the preset environmental database, a construction operation is performed to obtain a power consumption behavior and environment association mapping table.

[0077] In an implementation mode, the construction operation according to the preliminary power consumption behavior profile and the environmental data recorded by the preset environmental database to obtain the power consumption behavior and environment association mapping table includes steps S301 to S303:

[0078] S301, according to the preliminary power consumption behavior profile, combining the temperature fluctuation value, the humidity change index and the light intensity, the power consumption behavior is matched with the environmental data to obtain an initial data set containing the environmental data and the power consumption behavior.

[0079] It should be noted that the data in the preset environment database is the environment data collected at the corresponding time of the preliminary electricity consumption behavior profile; these environment data include: temperature value, humidity value, light intensity, and temperature fluctuation value calculated according to the temperature value, humidity change index calculated according to the humidity value, and light intensity change data calculated according to the light intensity.

[0080] It should be noted that the initial data set contains a mapping of environment data to electricity consumption behavior, reflecting electricity consumption behavior information under fine-grained environment data, each of which is: a seasonal time period, a temperature fluctuation value, a humidity change index, a light intensity change data, and a power load change value. The initial data set is an important data source for subsequent searching of historical data according to environmental factors. In addition to querying according to environmental data, different data can also be used for querying according to the results of subsequent processing.

[0081] S302, according to the initial data set and the seasonal time period, classifying seasonal fluctuations and behavior patterns, obtaining key features related to the environment data, and obtaining an intermediate mapping table containing the key features.

[0082] For example, the key features are the rules in the environment data that can affect the power consumption. For example, the key features related to the environment data can be: in summer afternoon, under the condition that humidity and light intensity are constant, the power consumption increases by about 0.8kW for every 5°C increase in temperature.

[0083] S303, according to the intermediate mapping table, integrating the environment data, and obtaining a final electricity consumption behavior and environment association mapping table.

[0084] It should be noted that the environment data includes temperature value, humidity value, and light intensity, and the electricity consumption behavior and environment association mapping table obtained by integration also includes temperature value and humidity value; for example, one of the electricity consumption behavior and environment association mapping table is: “summer-afternoon│ 30–35°C │ 60–70% │ 600–800LX │ 5-7kW │”, which indicates that in summer afternoon, when the temperature is between 30°C and 35°C, the air humidity is between 60% and 70%, and the light intensity is between 600LX and 800LX, the user's power load is between 5kW and 7KW, and at this time no control instruction is executed and there is no corresponding effect.

[0085] In step S103, the current environment data and the current power load at the current time are needed to be obtained, the corresponding historical power peak value is found in the electricity consumption behavior and environment association mapping table, and the deviation value of the current power load and the historical power peak value is calculated.

[0086] It should be noted that the comparison method of the finding method and S202 is the same, and the current environment data is used to find the historical power consumption peak value in the power consumption behavior and environment association mapping table, and the historical power consumption peak value indicates the reasonable range of power consumption load under the current environment data. In this embodiment, the formula for calculating the bias value should be: Bias=B-P, where Bias is the bias value, B is the current power consumption load, and P is the historical power consumption peak value. The bias value determines whether the current load needs to be adjusted subsequently, and is also one of the references for adjusting the specific value.

[0087] In step S104, when it is determined that the bias value exceeds the preset bias threshold and falls within the preset fluctuation interval, a preliminary energy-saving control parameter set containing the behavior adjustment signal and the adjustment amplitude needs to be generated according to the historical control archive obtained in advance.

[0088] In an implementation manner, when it is determined that the bias value exceeds the preset bias threshold and falls within the preset fluctuation interval, for example, in a certain scenario, the bias threshold is 15 KW, the preset fluctuation interval is 15.25 kW to 15.75 kW, and the current bias value is 15.5 kW, which is greater than the bias threshold and within the fluctuation interval. At this time, a preliminary energy-saving control parameter set containing the behavior adjustment signal and the adjustment amplitude is generated according to the historical control archive obtained in advance, including steps S401 to S404:

[0089] S401, comparing according to the fluctuation interval, obtaining distribution data of the fluctuation interval;

[0090] S402, if the distribution data meets the preset trigger condition, generating a corresponding behavior adjustment signal;

[0091] S403, matching the historical control archive according to the behavior adjustment signal, the environment data and the fluctuation interval, obtaining an initial adjustment amplitude;

[0092] S404, according to the power consumption behavior in the preliminary power consumption behavior archive, adjusting the initial adjustment amplitude within the fluctuation range of the power consumption behavior to obtain the final adjustment amplitude.

[0093] It should be noted that there are multiple bias values in a period, and a bias value set exceeding the preset bias threshold and falling within the preset fluctuation interval is extracted, and the distribution data of the bias value set in the fluctuation interval can be obtained according to the bias value set and the fluctuation interval, including bias value fluctuation amplitude, frequency and duration information.

[0094] It should be noted that the deviation threshold is pre-set, and the deviation value exceeding the threshold indicates that the current power load has exceeded the actual needs, causing waste, and the deviation threshold can be set to 8% of the historical peak value; the fluctuation interval is the fluctuation range of the deviation value summarized from the deviation values in the historical abnormal records, and the deviation value falling within the range indicates that the current power load is unreasonable.

[0095] It should be noted that the preset trigger condition refers to a set of rules for judging whether the load abnormal fluctuation needs intervention, including that the abnormal duration exceeds the threshold, the deviation amplitude is too large, the abnormal occurrence frequency is too high, or the tolerance limit under specific environmental conditions, etc.; once the trigger condition is met, the system will automatically enter the behavior adjustment stage and generate the corresponding behavior adjustment signal.

[0096] It should be noted that the behavior adjustment signal is to increase the load or reduce the load. At a more fine-grained control level, the signal can be further divided into enabling or disabling specific function modules, increasing or reducing power levels, etc. Operation modes to achieve precise adjustment of the overall load; the historical control archive records the behavior adjustment signal and its adjustment amplitude executed under different loads, for different environmental data and fluctuation intervals, and the effect after the adjustment signal is executed; wherein the effect after the adjustment signal is executed includes the power consumption behavior.

[0097] It should be noted that the power consumption behavior archive records the reasonable fluctuation range of the power load corresponding to the seasonal time period, and the adjustment amplitude should be limited within the range. For example, if the set adjustment amplitude is 9KW, but the power consumption behavior archive records that the fluctuation of the power load in the current seasonal time period is less than 8.5KW, therefore the adjustment amplitude should be reduced to 8.5kW. The current time is taken as the adjustment time, and the adjustment time, the behavior adjustment signal, the adjustment amplitude and the adjustment speed are integrated to obtain the preliminary energy-saving control parameter set.

[0098] In another implementation manner, when the deviation value does not exceed the preset deviation threshold or does not fall within the preset fluctuation interval, for example, the current deviation threshold is 15KW, the abnormal fluctuation range is 15.25kW to 35KW, if the deviation value is less than 15kW or the deviation value is 15kW to 15.25kW, it indicates that adjustment is not needed at this time. Similarly, the deviation value greater than 35kW means that the current data may be abnormal and should not be adjusted according to the abnormal data.

[0099] When it is determined that the deviation value does not exceed the preset deviation threshold value or does not fall into the preset fluctuation interval, it indicates that the current power consumption state is still within the acceptable range, and no behavior adjustment needs to be made to the current load. Alternatively, the current data is abnormal, and the obtained deviation value is too large, so the data should be filtered and not adjusted according to the deviation value. At this time, the existing running state can be maintained, and data monitoring and recording can be continued for subsequent trend analysis and model updating.

[0100] In step S105, in an implementation manner, the scene adaptation adjustment is made to the preliminary energy-saving control parameter set according to the temperature fluctuation value and the humidity change index to obtain a temporary energy-saving control parameter set, which includes steps S501 to S502:

[0101] S501, when the temperature fluctuation value exceeds the preset temperature fluctuation range or the humidity change index exceeds the preset humidity fluctuation range, the scene adaptation adjustment is made to the preliminary energy-saving control parameter set to obtain a temporary parameter set.

[0102] It should be noted that when the temperature fluctuation value exceeds the preset temperature fluctuation range or the humidity change index exceeds the preset humidity fluctuation range, it indicates that the current environment is not stable, and the load cannot be adjusted too fast and too large, otherwise the load will not be adapted due to the rapid changes of temperature and humidity, and therefore the scene adaptation adjustment needs to be made to the preliminary energy-saving control parameter set. For example, the current temperature fluctuation value is ±5℃, and the preset temperature fluctuation range is -3℃ to 3℃, which indicates that the current temperature changes too fast, and the adjustment of the load, especially the temperature-related load, should not be too fast and too large. If the adjustment amplitude or the adjustment speed of the energy-saving control parameter set is too large or too fast, the adjustment should be made. The specific adjustment means includes reducing the adjustment amplitude, delaying the adjustment time, and delaying the adjustment speed.

[0103] S502, the target power consumption behavior is extracted from the historical control archive according to the temporary parameter set, and if the target power consumption behavior does not meet the preset scene requirement, the temporary parameter set is optimized according to the target power consumption behavior to obtain a temporary energy-saving control parameter set.

[0104] It should be noted that in this embodiment, the matching manner is selected in step S202, and the control instruction execution record in the historical control archive that is most similar to the current temporary parameter set is found, and the power consumption behavior after the execution of the instruction is obtained. The specific process is consistent with step S202, and will not be described here.

[0105] The preset scene requirement, i.e. the energy saving requirement of the environment in the current season time period, limits the range of the power consumption load. In an embodiment, when the target power consumption behavior does not meet the preset scene requirement, for example, the power consumption load of the target power consumption behavior is 9KW, while the preset scene requirement requires the power consumption load to be lower than 8.5KW, the power consumption load of the target power consumption behavior is still too high, and the temporary parameter set needs to be optimized, which is a compensation adjustment of step S501. The optimization mode includes increasing the adjustment amplitude and accelerating the adjustment rate, so as to further reduce the power consumption load and obtain the temporary energy saving control parameter set.

[0106] In another embodiment, when the target power consumption behavior meets the preset scene requirement, it means that the temporary parameter set has good energy saving control effect and environmental adaptability, and no compensation adjustment of amplitude increase is needed at this time. At this time, the temporary parameter set is directly used as the temporary energy saving control parameter set.

[0107] In another implementation, when the temperature fluctuation value does not exceed the preset temperature fluctuation range and the humidity change index does not exceed the preset humidity fluctuation range, it means that the temperature and humidity change slowly, and no scene adaptation adjustment is needed. The preliminary energy saving control parameter set is the temporary energy saving control parameter set.

[0108] Step S106 needs to obtain the running state data of the real-time power consumption equipment, and match it with the preset equipment performance constraint condition.

[0109] It should be noted that the running state data of the real-time power consumption equipment includes the current working mode, output power, current, voltage, start-stop state, running temperature, response rate, load rate and other key parameters of the equipment. These data can be collected in real time through smart meters, sensors, device self-monitoring modules or energy management systems. In this embodiment, the preset equipment constraint condition is the upper limit of the load rate, for example, the upper limit of the safe load rate of a certain equipment is 80%. When the collected real-time running data shows that the equipment load rate exceeds the upper limit, it is considered that the equipment is out of the safe operating range, which may cause risks such as overheating, performance degradation or abnormal shutdown of the equipment; after collecting these running state data, the system compares them with the preset equipment performance constraint condition to determine whether the control strategy of the current temporary energy saving control parameter set is within the safe operating range of the equipment.

[0110] In an implementation, if the equipment performance constraint condition is not met, the temporary energy saving control parameter set is calibrated again according to the adjustment amplitude and the fluctuation interval to obtain an optimized energy saving control parameter set, including:

[0111] S601, if the fluctuation range exceeds the preset device performance constraint condition range, dynamically adjusting the temporary energy-saving control parameter set based on the adjustment amplitude and the environmental data to determine a calibrated parameter set;

[0112] It should be noted that the fluctuation range exceeding the preset device performance constraint condition range means that the preset fluctuation range in S104 cannot meet the requirements of the real-time power consumption device, and therefore the temporary energy-saving control parameter set obtained needs to be calibrated.

[0113] For example, the power consumption load of an electric appliance is 9KW, the maximum load of the device is 10KW, i.e. the device load rate is 90%, the device performance constraint condition is that the load rate is less than 80%, i.e. the power consumption load is less than 8KW, the fluctuation range is 8.5kW to 10KW, and the behavior adjustment signal obtained after the step is executed is to reduce the load by 0.5KW, and the power consumption load can only be reduced to 8.5KW, which still cannot meet the device performance constraint condition, and therefore the adjustment amplitude needs to be changed to 1kW. If the electric appliance is a cooling device, the temperature value of the environmental data at that time does not exceed 25℃, which belongs to a relatively comfortable environment, and because the adjustment amplitude is 1kW, the operating state of the cooling device just meets the device performance constraint condition, and therefore the power consumption load can be further reduced, and the adjustment amplitude is changed to 1.25kW.

[0114] The calibrated parameter set contains parameters of control instructions, including the behavior adjustment signal, the adjustment amplitude, the adjustment time and the adjustment speed. The behavior adjustment signal here is to reduce the load, and the control signal for reducing the load can be more granularly divided into reducing the power setting or suspending part of the function module, and the specific power reduction value and the number of suspended module functions are determined by the adjustment amplitude. The higher the adjustment amplitude, the higher the power reduction value and the more the number of suspended module functions.

[0115] S602, according to the calibrated parameter set, obtaining a matching target operating mode from the historical control archive, and if the target operating mode meets the preset operating mode requirement, determining the optimized energy-saving control parameter set according to the calibrated parameter set;

[0116] It should be noted that the target operating mode refers to the device operating state and power consumption load response corresponding to the control parameters closest to the calibrated parameter set in the historical control archive, which is used to reflect the actual operating effect of the device under similar control conditions.

[0117] It should be noted that the preset operation mode requirement refers to a standardized operation reference condition formulated according to different equipment types, seasonal time periods, external environments, and safety, energy saving, and the like in the design stage, including equipment operation states and power load response conditions. For example, if the load rate of the target operation mode is 50%, and the operation mode requirement of the current equipment type is that the load rate is less than 80%, it can be indicated that the operation mode requirement is met.

[0118] In an implementation manner, the target operation mode meets the preset operation mode requirement, and the calibrated parameter set is used as the optimized energy-saving control parameter set for subsequent steps.

[0119] In another implementation manner, the target operation mode does not meet the preset operation mode requirement, for example, the load rate of the target operation mode exceeds the load rate of the operation mode requirement, which indicates that the calibrated parameter set still needs to be adjusted, and the load should be further reduced. At this time, the adjustment amplitude and the adjustment speed are slightly increased, and S602 is executed again for iteration until the preset operation mode requirement is met.

[0120] In another implementation manner, if the equipment performance constraint condition is met, it indicates that the current temporary energy-saving control parameter set has executability and stability, and can be directly used as the final energy-saving control parameter set without further calibration. At this time, the system can enter a parameter application stage, and the temporary energy-saving control parameter set is directly used as the optimized energy-saving control parameter set for subsequent steps.

[0121] In step S107, an energy-saving control instruction is generated according to the optimized energy-saving control parameter set in combination with the light intensity and the seasonal time period, and an execution order of the energy-saving control instruction is determined.

[0122] In an implementation manner, the energy-saving control instruction is generated according to the optimized energy-saving control parameter set in combination with the light intensity and the seasonal time period, and the execution order of the energy-saving control instruction is determined, including steps S701 to S708.

[0123] S701, selecting a matching light threshold range from a preset seasonal light threshold library according to the seasonal time period;

[0124] S702, if the light intensity exceeds the light threshold range, adjusting the optimized energy-saving control parameter set to obtain a preliminary environment adaptation scheme;

[0125] S703, obtaining a current weather mode category;

[0126] S704, obtaining an equipment type of the real-time power consumption equipment according to the real-time power consumption equipment in combination with preset classification data of different equipment.

[0127] S705, matching the historical power consumption peak of the device type from the preliminary power consumption behavior profile according to the device type and the seasonal time period;

[0128] S706, obtaining the current device type power consumption load, and calculating a load deviation value of the historical power consumption peak and the current device type power consumption load;

[0129] S707, determining the order of delivery according to the preliminary environmental adaptation scheme, the load deviation value and the current weather mode category, according to a preset priority rule, to obtain a classified energy-saving control instruction priority set;

[0130] S708, parameter matching according to the classified energy-saving control instruction priority set and the preset energy-saving target of the device type, to obtain an energy-saving control instruction.

[0131] It should be noted that the seasonal light threshold library of S701 is a standardized reference data set formed by statistical modeling of long-term collected light sensing data according to the typical natural light intensity level of different seasons and daytime time intervals. The threshold library is divided by season and typical time period, and each combination item corresponds to a reasonable light intensity range, which is used to judge whether the current light condition is abnormal or too strong / weak, so as to assist in judging whether the lighting device or sunshade device needs to intervene in energy-saving control.

[0132] It should be noted that the preliminary environmental adaptation scheme of S702 is a set of control strategies adjusted and corrected based on the original energy-saving control parameter set. This scheme combines the influence of current light changes on power consumption behavior, dynamically adjusts the adjustment amplitude and adjustment speed in the control parameters, to enhance the adaptability of energy-saving strategies to the actual environment. For example, in the strong light period, the original control scheme can be improved by reducing the lighting intensity, enabling the sunshade device in advance, etc., to form a control scheme more suitable for the current light scene, providing a strategy basis for subsequent generation of energy-saving control instructions.

[0133] It should be noted that the weather mode category of S703 is specifically: high temperature sunny type, high temperature and high humidity type, rainy and humid type, low temperature and dry type, and cloudy and weak light type; the current weather mode category can be one or more of them.

[0134] It should be noted that the device type of S704 can be one of the following: cooling devices, lighting devices, ventilation and air handling devices, heating devices, sunshade devices, office and industrial load devices, elevators and transportation devices, emergency devices and other devices; different device types correspond to different control logic and response characteristics.

[0135] It should be noted that the type of the current target device and the seasonal time period to which the current time belongs are taken as joint search conditions to search for the corresponding historical record in the preliminary power consumption behavior archive in S705, so as to obtain the historical power consumption peak of the device of this category under similar conditions as the benchmark value for subsequent comparative analysis.

[0136] It should be noted that the current device type power consumption load can be obtained in real time by the energy consumption monitoring module in S706, including instantaneous power, current, voltage, etc. The system compares the obtained current load value with the historical power consumption peak, calculates the difference value, called load bias value, for quantifying whether the current device has load abnormality or power deviation. The load bias value calculation formula is: Bias_device=C-H. Wherein, Bias_device is the load bias value, C is the current device type power consumption load, and H is the current device type historical power consumption peak.

[0137] It should be noted that the preliminary environment adaptation scheme contains multiple control instructions in S707; the higher the load bias value or the more extreme the current weather mode category, the more rapidly the adjustment is required, and the higher the priority; the control instructions are classified according to the last obtained priority to obtain the classified energy-saving control instruction priority set.

[0138] It should be noted that the parameter matching in S708 means that each instruction in the classified energy-saving control instruction priority set is compared with the energy-saving target of the corresponding device type under the preset scene, and if it does not meet the energy-saving target, the control parameters are fine-tuned in combination with the current load state and priority, so as to generate energy-saving control instructions with complete structure and direct execution.

[0139] In an embodiment, after step S107, the energy-saving control instructions are also needed to be issued according to the execution order of the energy-saving control instructions in the priority order; it is judged whether there is abnormal feedback, if there is, the execution order of the energy-saving control instructions is adjusted to obtain the final issuance execution result.

[0140] It should be noted that the abnormal feedback can be one of execution failure, instruction timeout, device fault alarm, load overshoot / undershoot, communication interruption and other faults, which can reduce the priority of abnormal instructions, skip the execution of abnormal instructions or intervene in manual processing.

[0141] In summary, the application discloses an energy-saving control method of an electric energy meter, comprising the following steps: obtaining a preliminary electricity consumption behavior profile containing historical electricity consumption peak values and seasonal time periods; constructing a mapping table of electricity consumption behavior and environment according to the preliminary electricity consumption behavior profile and environmental data recorded in a preset environment database, wherein the environmental data comprises temperature fluctuation values, humidity change indexes and light intensity; obtaining the environmental data and current electricity consumption load at the current time, finding the corresponding historical electricity consumption peak value in the mapping table of electricity consumption behavior and environment, and calculating the deviation value of the current electricity consumption load and the historical electricity consumption peak value; when it is determined that the deviation value exceeds a preset deviation threshold value and falls within a preset fluctuation interval, generating a preliminary energy-saving control parameter set containing behavior adjustment signals and adjustment amplitudes according to a historical control profile obtained in advance; performing scene adaptive adjustment on the preliminary energy-saving control parameter set according to the temperature fluctuation values and the humidity change indexes, to obtain a temporary energy-saving control parameter set; obtaining the running state data of real-time electricity consumption equipment, and matching the running state data with preset equipment performance constraint conditions; if the equipment performance constraint conditions are not met, performing secondary calibration on the temporary energy-saving control parameter set according to the adjustment amplitudes and the fluctuation interval, to obtain an optimized energy-saving control parameter set; generating an energy-saving control instruction according to the optimized energy-saving control parameter set, in combination with the light intensity and the seasonal time periods, and determining the execution order of the energy-saving control instruction.

[0142] The application realizes adaptive and accurate energy-saving control by distinguishing multiple users, multiple devices and multiple scenes, and accurately and timely fine-tuning the current electricity consumption load of different devices in combination with historical electricity consumption information. In addition, the amplitude and speed of the energy-saving control instruction are corrected according to the external environment fluctuation range, which avoids the situation that the adjusted load does not meet the requirements of environmental data, and further improves the environmental adaptability and stability of energy-saving control.

[0143] Reference Figure 2 The second facility of the application provides an energy-saving control system of an electric energy meter, which can implement all processes of the energy-saving control method of the electric energy meter, comprising:

[0144] M01, a data acquisition module, obtaining a preliminary electricity consumption behavior profile containing historical electricity consumption peak values and seasonal time periods;

[0145] M02, a mapping construction module, constructing a mapping table of electricity consumption behavior and environment according to the preliminary electricity consumption behavior profile and environmental data recorded in a preset environment database, wherein the environmental data comprises temperature fluctuation values, humidity change indexes and light intensity;

[0146] M03, difference calculation module, obtaining current environment data and current power load at the current time, finding the corresponding historical power peak in the power consumption behavior and environment association mapping table, and calculating the deviation value of the current power load and the historical power peak;

[0147] M04, parameter acquisition module, when it is determined that the deviation value exceeds the preset deviation threshold and falls into the preset fluctuation interval, and according to the historical control archives obtained in advance, a preliminary energy-saving control parameter set containing behavior adjustment signal and adjustment amplitude is generated;

[0148] M05, parameter adjustment module, according to the temperature fluctuation value and the humidity change index, the preliminary energy-saving control parameter set is scene-adapted and adjusted to obtain a temporary energy-saving control parameter set;

[0149] M06, secondary calibration module, obtaining the running state data of the real-time power consumption equipment, matching with the preset equipment performance constraint condition, if the equipment performance constraint condition is not met, the temporary energy-saving control parameter set is secondarily calibrated according to the adjustment amplitude and the fluctuation interval to obtain an optimized energy-saving control parameter set;

[0150] M07, instruction generation module, according to the optimized energy-saving control parameter set, combining the illumination intensity and the seasonal time period to generate an energy-saving control instruction, and determining the execution order of the energy-saving control instruction.

[0151] It should be noted that the energy-saving control system of the electric energy meter provided by the embodiment of the present application is used to execute all process steps of the energy-saving control method of the electric energy meter in the above embodiment, and the working principles and beneficial effects of the two are one-to-one correspondence, so it will not be repeated.

[0152] The embodiment of the present application also provides an electronic device. The electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor, for example, a difference calculation program. The processor implements the steps in the above-mentioned various energy-saving control method embodiments of the electric energy meter when executing the computer program, for example Figure 1 The steps S11 shown. Alternatively, the processor implements the functions of each module / unit in the above-mentioned various device embodiments when executing the computer program, for example, the secondary calibration module.

[0153] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0154] The electronic device can be a computing device such as a desktop computer, a notebook computer, a palm computer, a smart tablet, etc. The electronic device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than the above, or combine certain components, or different components, for example, the electronic device can also include an input / output device, a network access device, a bus, etc.

[0155] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The processor is the control center of the electronic device, and connects various parts of the electronic device through various interfaces and lines.

[0156] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function (such as a sound playing function, an image playing function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0157] The modules / units integrated in the electronic device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0158] It should be noted that the above-described device embodiments are only schematic, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0159] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are only for the specific embodiments of the present application and are not used to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for energy saving control of an electric energy meter, characterized by, The method comprises the following steps: acquiring a preliminary electricity consumption behavior profile containing historical electricity consumption peaks and seasonal time periods; performing a construction operation according to the preliminary electricity consumption behavior profile and environmental data recorded in a preset environmental database to obtain an electricity consumption behavior and environment association mapping table; wherein the environmental data comprises temperature fluctuation values, humidity change indexes and illumination intensities; acquiring current environmental data and a current electricity consumption load at a current time, finding a corresponding historical electricity consumption peak in the electricity consumption behavior and environment association mapping table, and calculating a deviation value of the current electricity consumption load and the historical electricity consumption peak; when it is determined that the deviation value exceeds a preset deviation threshold value and falls within a preset fluctuation interval, generating a preliminary energy-saving control parameter set containing a behavior adjustment signal and an adjustment amplitude according to a historical control profile acquired in advance; performing scene adaptation adjustment on the preliminary energy-saving control parameter set according to the temperature fluctuation values and the humidity change indexes to obtain a temporary energy-saving control parameter set; acquiring running state data of real-time electricity consumption equipment, matching with preset equipment performance constraint conditions, if the equipment performance constraint conditions are not met, performing secondary calibration on the temporary energy-saving control parameter set according to the adjustment amplitude and the fluctuation interval to obtain an optimized energy-saving control parameter set; generating an energy-saving control instruction according to the optimized energy-saving control parameter set, combining the illumination intensity and the seasonal time period, and determining an execution order of the energy-saving control instruction.

2. The energy saving control method of an electric energy meter according to claim 1, wherein The method comprises the following steps: extracting multi-dimensional electricity consumption information from a preset electricity consumption profile, and performing classification processing on electricity consumption behavior modes of different users in specific scenes to obtain classified multi-dimensional electricity consumption information; comparing the classified multi-dimensional electricity consumption information with a preset electricity consumption feature database to obtain the preliminary electricity consumption behavior profile containing historical electricity consumption peaks and seasonal time periods.

3. The energy saving control method of an electric energy meter according to claim 1, wherein The method comprises the following steps: matching the electricity consumption behavior with the environmental data according to the preliminary electricity consumption behavior profile, combining the temperature fluctuation values, the humidity change indexes and the illumination intensities to obtain an initial data set containing the environmental data and the electricity consumption behavior; classifying seasonal fluctuations and behavior modes according to the initial data set and the seasonal time periods, acquiring key features related to the environmental data to obtain an intermediate mapping table containing the key features; integrating the environmental data according to the intermediate mapping table to obtain a final electricity consumption behavior and environment association mapping table.

4. The energy saving control method of an electric energy meter according to claim 1, wherein The method comprises the following steps: comparing the fluctuation interval to obtain distribution data of the fluctuation interval; if the distribution data meets a preset triggering condition, generating a corresponding behavior adjustment signal; matching the historical control profile according to the behavior adjustment signal, the environmental data and the fluctuation interval to obtain an initial adjustment amplitude; According to the power consumption behavior in the preliminary power consumption behavior archive, the initial adjustment range is modified within a fluctuation range of the power consumption behavior, to obtain a final adjustment range.

5. The energy saving control method of an electric energy meter according to claim 4, wherein The scene adaptive adjustment of the preliminary energy-saving control parameter set according to the temperature fluctuation value and the humidity change index obtains a temporary energy-saving control parameter set, which comprises: When the temperature fluctuation value exceeds a preset temperature fluctuation range or the humidity change index exceeds a preset humidity fluctuation range, the scene adaptive adjustment of the preliminary energy-saving control parameter set obtains a temporary parameter set; According to the temporary parameter set, a matching target power consumption behavior is extracted from the historical control archive, and if the target power consumption behavior does not meet the preset scene requirements, the temporary parameter set is optimized according to the target power consumption behavior, to obtain a temporary energy-saving control parameter set.

6. The energy saving control method of an electric energy meter according to claim 4, wherein The secondary calibration of the temporary energy-saving control parameter set according to the adjustment range and the fluctuation range obtains an optimized energy-saving control parameter set, which comprises: If the fluctuation range exceeds a preset device performance constraint condition range, the temporary energy-saving control parameter set is dynamically adjusted based on the adjustment range and the environmental data to determine a calibrated parameter set; According to the calibrated parameter set, a matching target running mode is obtained from the historical control archive, and if the target running mode meets the preset running mode requirements, the optimized energy-saving control parameter set is determined according to the calibrated parameter set.

7. The energy saving control method of an electric energy meter according to claim 6, wherein The energy-saving control instruction is generated according to the optimized energy-saving control parameter set in combination with the light intensity and the seasonal time period, which comprises: According to the seasonal time period, a matching light threshold range is selected from a preset seasonal light threshold library; If the light intensity exceeds the light threshold range, the optimized energy-saving control parameter set is adjusted to obtain a preliminary environmental adaptation scheme; A current weather mode category is obtained; According to the real-time power consumption device, a device type of the real-time power consumption device is obtained in combination with preset classification data of different devices; According to the device type and the seasonal time period, a historical power consumption peak value of the device type is matched from the preliminary power consumption behavior archive; A current device type power consumption load is obtained, and a load deviation value of the historical power consumption peak value and the current device type power consumption load is calculated; According to the preliminary environmental adaptation scheme, the load deviation value and the current weather mode category, a delivery order is determined according to a preset priority rule, to obtain a classified energy-saving control instruction priority set; According to the classified energy-saving control instruction priority set and a preset energy-saving target of the device type, a parameter matching is performed to obtain an energy-saving control instruction.

8. The energy saving control method of an electric energy meter according to claim 1, wherein After the execution order of the energy-saving control instruction is determined, the method further comprises: According to the execution order of the energy-saving control instruction, the energy-saving control instruction is delivered in priority order; It is judged whether there is an abnormal feedback, and if there is, the execution order of the energy-saving control instruction is adjusted to obtain a final delivery execution result.

9. An energy saving control system of an electric energy meter, characterized by, It comprises: The data acquisition module acquires a preliminary electricity consumption behavior profile containing historical electricity consumption peak and seasonal time period; The mapping construction module constructs a mapping table of electricity consumption behavior and environment according to the preliminary electricity consumption behavior profile and environmental data recorded in a preset environment database, wherein the environmental data includes temperature fluctuation value, humidity change index and illumination intensity; The difference calculation module acquires current environmental data and current electricity consumption load at a current time, finds corresponding historical electricity consumption peak in the mapping table of electricity consumption behavior and environment, and calculates a deviation value of the current electricity consumption load and the historical electricity consumption peak; The parameter acquisition module generates a preliminary energy-saving control parameter set containing behavior adjustment signal and adjustment amplitude according to a historical control profile acquired in advance when the deviation value is determined to exceed a preset deviation threshold and fall into a preset fluctuation interval; The parameter adjustment module performs scene adaptation adjustment on the preliminary energy-saving control parameter set according to the temperature fluctuation value and the humidity change index, and obtains a temporary energy-saving control parameter set; The secondary calibration module acquires running state data of real-time electricity consumption equipment, matches with a preset equipment performance constraint condition, and performs secondary calibration on the temporary energy-saving control parameter set according to the adjustment amplitude and the fluctuation interval to obtain an optimized energy-saving control parameter set if the equipment performance constraint condition is not met; The instruction generation module generates an energy-saving control instruction according to the optimized energy-saving control parameter set, combines the illumination intensity and the seasonal time period, and determines an execution order of the energy-saving control instruction.

10. A computer-readable storage medium storing a computer program, characterized in that, The program is executed by the processor to implement the steps of the method of any one of claims 1 to 8.

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