Industrial electrical load adjustable potential prediction method, system and device and storage medium

Abstract

The invention discloses an industrial electrical load adjustable potential prediction method, system and device, and a storage medium, and the specific implementation scheme is as follows: obtaining a work log of a target device, and determining the high-load operation of the target device in different operation periods according to the work log; determining accidental high-load operation according to the coherence condition of the high-load operation in different operation periods and marking the operation periods; according to the accidental high-load operation, obtaining a first prepositive normal load operation of the marked operation cycle and a second prepositive normal load operation of other operation cycles in the work log; comparing the operation data of the first preposed normal load operation and the operation data of the second preposed normal load operation to obtain remarkable operation characteristics; and determining a difference degree value corresponding to the significant operation feature, and determining predicted electric energy data based on the difference degree value and actual operation data corresponding to the accidental high-load operation in the work log. According to the embodiment of the invention, accurate prediction of the power load is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial power load prediction, and in particular to a method, system, device and storage medium for predicting the adjustable potential of industrial power load. Background Art

[0002] In the industrial production process, the stability of power load is crucial to the normal operation of equipment. In order to maintain the normal operation of equipment, industrial power load forecasting technology plays a key role in the industrial production process. In large-scale, high-energy consumption production processes, the fluctuation of power demand directly affects the operating efficiency of equipment and the quality of products. If high-load operations are not predicted in a timely and accurate manner and sufficient power support is not provided for high-load operations, it will lead to equipment failure, shutdown, and even production accidents. At present, the traditional industrial power load forecasting method is mainly based on historical data and regular load changes to predict conventional and coherent load operations. However, there are sporadic high-load operations in the industrial production process. Due to the randomness and unpredictability of sporadic high-load operations, the traditional industrial power load forecasting method cannot identify sporadic high-load operations, resulting in the inability of traditional power load forecasting methods to accurately regulate power supply according to actual needs. As a result, when sporadic high-load operations occur, the equipment will frequently overload due to insufficient power supply, increasing the risk of equipment failure and production interruption. Summary of the invention

[0003] The present invention provides a method, system, device and storage medium for predicting the adjustable potential of industrial power load. The embodiments of the present invention realize accurate prediction of power load.

[0004] In one aspect of an embodiment of the present invention, a method for predicting the adjustable potential of industrial power load is provided, comprising:

[0005] Obtain the work log of the target device, and determine the high-load operation of the target device in different operation cycles according to the work log;

[0006] Determine occasional high-load operations and mark operation cycles based on the consistency of high-load operations in different operation cycles;

[0007] Acquire the first preceding normal load operation of the marked operation cycle and the second preceding normal load operation of other operation cycles in the work log according to the occasional high load operation;

[0008] Comparing the operation data of the first front-end normal load operation and the second front-end normal load operation to obtain significant operation characteristics;

[0009] Determine the difference degree value corresponding to the significant operating characteristics, and determine the predicted power data based on the difference degree value and the actual operating data corresponding to the occasional high-load operation in the work log.

[0010] Another aspect of the embodiments of the present invention provides a system for predicting the adjustable potential of industrial power load, including:

[0011] A high-load operation extraction module, used to obtain the work log of the target device, and determine the high-load operations of the target device in different operation cycles according to the work log;

[0012] A continuity judgment module, used to determine occasional high-load operations and mark operation cycles according to the continuity of high-load operations in different operation cycles;

[0013] An operation extraction module is used to obtain the first preceding normal load operation of the marked operation cycle and the second preceding normal load operation of other operation cycles in the work log according to the occasional high load operation;

[0014] a difference determination module, for comparing the operation data of the first front normal load operation and the second front normal load operation to obtain a significant operation feature;

[0015] The electric energy prediction module is used to determine the difference degree value corresponding to the significant operating characteristics, and determine the predicted electric energy data based on the difference degree value and the actual operating data corresponding to the occasional high-load operation in the work log.

[0016] Another aspect of an embodiment of the present invention provides a device, including:

[0017] at least one processor;

[0018] and a memory communicatively coupled to the at least one processor;

[0019] The memory stores a computer program executable by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the method for predicting the adjustable potential of industrial electricity load as described in any one of claims 1-7.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting the adjustable potential of industrial power load of any embodiment of the present invention when executed.

[0021] According to an embodiment of the present invention, a work log of a target device is obtained from the target device, and high-load operations of the target device in different operation cycles are searched in the work log, the continuity of high-load operations in different operation cycles is judged, high-load operations without continuity are determined as occasional high-load operations, and operation cycles containing occasional high-load operations are determined as marked operation cycles. According to the occasional high-load operations, a first pre-normal load operation in the marked operation cycle and a second pre-normal load operation in other operation cycles are searched in the work log, operation data of the first pre-normal load operation and operation data of the second pre-normal load operation are obtained, the operation data of the first pre-normal load operation and the operation data of the second pre-normal load operation are compared, and significant operation features between the operation data are obtained. Based on the significant operation features, a difference degree value between the operation data of the first pre-normal load operation and the operation data of the second pre-normal load operation corresponding to the significant operation features is determined, actual operation data of the target device when the occasional high-load operation occurs is searched in the work log, predicted power data of the target device is determined according to the difference degree value and the actual operation data, and subsequent operations are performed based on the predicted power data. The embodiment of the present invention determines the sporadic high-load operation through the continuity of the high-load operation, thereby achieving accurate distinction of the sporadic high-load operation, extracts significant operating features based on the operating data of the first preceding normal load operation and the operating data of the second preceding normal load operation of the sporadic high-load operation, determines the difference degree value based on the significant operating features, and determines the predicted electric energy data of the target equipment according to the difference degree value, thereby achieving accurate prediction of the power load, ensuring the rationality of the power allocation strategy, and improving the safety of equipment operation.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 This is a flow chart of a method for predicting the adjustable potential of industrial power load provided according to the first embodiment of the present invention;

[0025] Figure 2 is a flow chart of another method for predicting the adjustable potential of industrial power load provided according to the second embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of the structure of an industrial power load adjustable potential prediction system for implementing an embodiment of the present invention;

[0027] Figure 4 It is a structural schematic diagram of a high-load operation extraction module in an industrial power load adjustable potential prediction system for implementing an embodiment of the present invention;

[0028] Figure 5 It is a schematic diagram of the structure of a continuity judgment module in an industrial power load adjustable potential prediction system for implementing an embodiment of the present invention;

[0029] Figure 6 It is a schematic diagram of the structure of an operation extraction module in an industrial power load adjustable potential prediction system for implementing an embodiment of the present invention;

[0030] Figure 7 It is a schematic diagram of the structure of a difference determination module in an industrial power load adjustable potential prediction system for implementing an embodiment of the present invention;

[0031] Figure 8 It is a schematic diagram of the structure of an electric energy prediction module in an industrial electric load adjustable potential prediction system to implement an embodiment of the present invention;

[0032] Fig. 9 It is a block diagram of a device for executing the method for predicting the adjustable potential of industrial power load according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0035] Embodiment 1

[0036] Figure 1 A flowchart of a method for predicting the adjustable potential of industrial power load is provided for the first embodiment of the present invention. The embodiment of the present invention can be applied to predicting power load. The method can be executed by a system for predicting the adjustable potential of industrial power load. The system can be implemented in the form of hardware and / or software. The system can be configured in a device. Figure 1 As shown, the method includes:

[0037] S101. Obtain a work log of a target device, and determine high-load operations of the target device in different operation cycles according to the work log.

[0038] In the embodiment of the present invention, the work log can be understood as a document for recording the operation data of the target device in each operation cycle. For example, the work log may include: the high-load operation of the target device in different operation cycles, the power consumption of the target device, the operation steps of the target device, the timestamp of the high-load operation, the working status of the target device, the temperature of the target device or the pressure value of the target device. The work log provides basic data for analyzing the operation status of the equipment, predicting the load demand and performing preventive maintenance.

[0039] An operation cycle may be understood as a time period for a target device to complete a complete operation. For example, the types of operation cycles may include: a startup operation cycle, a processing operation cycle, or a maintenance operation cycle.

[0040] High-load operation refers to an operation that puts the target device at its performance limit during operation. For example, high-load operation may include: extremely long-time operation or frequent on / off operation.

[0041] Specifically, a work log of the target device is obtained from the target device, and the work log records the operation data of the target device in each operation cycle. According to the obtained work log of the target device, the high-load operation of the target device in different operation cycles is found in the work log, wherein the high-load operation is an operation that instructs the target device to be at the performance limit. For example, the method of obtaining the work log of the target device from the target device may include: obtaining it through the log function of the target device itself or obtaining it through third-party software on the target device.

[0042] For example, in the field of chemical production, the target equipment may include: a reactor, and the high-load operation of the reactor may include: a startup operation, a reaction operation, and a cooling operation. The above operations cover the target equipment, i.e., the reactor, and specific operations such as preheating and pressurizing during startup, adding materials and stirring during the reaction, and cooling and decompression after the reaction.

[0043] S102: Determine occasional high-load operations and mark operation cycles according to the continuity of high-load operations in different operation cycles.

[0044] In the embodiment of the present invention, the continuity situation can be understood as a regularity in the time distribution of each high-load operation. For example, if each high-load operation occurs according to a fixed cycle or a fixed time interval, it is considered that the high-load operation has regularity in the time distribution, and it is determined that the high-load operation has continuity.

[0045] The occasional high-load operation can be understood as a high-load operation that lacks time continuity, and the marked operation cycle refers to an operation cycle that includes the occasional high-load operation.

[0046] Specifically, high-load operations of different operation cycles are obtained, and the time continuity of high-load operations of different operation cycles is determined. According to the continuity of the high-load operation, it can be determined whether the high-load operation is an occasional high-load operation, and it can be determined whether the operation cycle including the high-load operation is set as a marked operation cycle. If the high-load operation does not have continuity, the high-load operation is determined to be an occasional high-load operation, and the operation cycle including the high-load operation is determined to be a marked operation cycle.

[0047] S103, obtaining the first preceding normal load operation of the marked operation cycle and the second preceding normal load operation of other operation cycles in the work log according to the occasional high load operation.

[0048] In the embodiment of the present invention, the first pre-normal load operation can be understood as the most recent operation that brings the target device into a normal range before the occasional high load operation, wherein the first pre-normal load operation is included in the marking operation cycle.

[0049] The second pre-normal load operation can be understood as a normal load operation of the same type as the first pre-normal load operation in other operation cycles, wherein other operation cycles can be understood as other non-marked operation cycles except the marked operation cycles.

[0050] Specifically, based on the continuity of high-load operations, occasional high-load operations and marked operation cycles can be determined. According to the determined occasional high-load operations, the work log of the target device is searched for the first pre-normal load operation that occurred before the occasional high-load operation and was the most recent operation that put the target device within the normal range, as well as the second pre-normal load operation of the same type as the first pre-normal load operation in other operation cycles.

[0051] S104: Compare the operation data of the first front-end normal load operation and the second front-end normal load operation to obtain significant operation characteristics.

[0052] In the embodiment of the present invention, the operating data may be understood as data generated by the target device during operation. For example, the operating data may include: the temperature of the target device or the power consumption of the target device.

[0053] The significant operating characteristics can be understood as data indicators with the most prominent offset between operating data, wherein the significant operating characteristics at least include: data indicators with the most prominent offset between the first front-end normal load operation operating data and the second front-end normal load operation operating data.

[0054] Specifically, the operating data of the first front normal load operation and the operating data of the second front normal load operation are obtained, and the operating data of the first front normal load operation are compared with the operating data of the second front normal load operation to obtain the data indicator with the most prominent offset between the operating data of the first front normal load operation and the second front normal load operation, and the data indicator with the most prominent offset between the operating data of the first front normal load operation and the second front normal load operation is used as a significant operating feature of the target device.

[0055] For example, in the field of chemical production, the operating data of the target equipment reactor may include: operating speed or power consumption. The operating speed and power consumption of the first pre-normal load operation and the second pre-normal load operation are compared. By comparison, the data indicator with the most prominent offset between the operating speed and power consumption of the first pre-normal load operation and the second pre-normal load operation is the operating speed. Then the operating speed is used as a significant operating feature of the target equipment.

[0056] S105: Determine a difference degree value corresponding to a significant operating feature, and determine predicted power data based on the difference degree value and actual operating data corresponding to occasional high-load operations in a work log.

[0057] In the embodiment of the present invention, the difference degree value may be understood as a data offset, which is used to measure the difference degree between the running data of the first front-end normal load operation and the running data of the second front-end normal load operation.

[0058] The actual operation data can be understood as the operation data of the target device found in the work log when the occasional high-load operation occurs. For example, the actual operation data may include: the internal temperature of the target device is 53 degrees or the power consumption of the target device is 200 watts. It is understandable that the actual operation data is obtained by analyzing the power consumption of the target device during the occasional high-load operation recorded in the historical work log. The work log records in detail the power usage of the target device in each operation cycle. By extracting this data, the specific power value consumed by the target device when performing occasional high-load operations can be determined, and this data provides a basis for subsequent power allocation.

[0059] The predicted power data can be understood as the energy consumption data required for the normal operation of the target equipment. Based on the predicted power data, the power load can be accurately allocated.

[0060] Specifically, significant operating characteristics are obtained between the operating data of the first pre-normal load operation and the second pre-normal load operation, and the difference between the two groups of operating data is obtained based on the obtained significant operating characteristics. The difference between the two groups of operating data is defined as a difference degree value, and the actual operating data of the target device when occasional high-load operations occur in the work log is searched, and based on the actual operating data, the predicted electric energy data of the target device is determined according to the difference degree value, and subsequent operations are performed according to the predicted electric energy data of the target device. By way of example, the subsequent operations at least include: power load allocation.

[0061] According to an embodiment of the present invention, a work log of a target device is obtained from the target device, and high-load operations of the target device in different operation cycles are searched in the work log, the continuity of high-load operations in different operation cycles is judged, and high-load operations without continuity are determined as sporadic high-load operations, and operation cycles containing sporadic high-load operations are determined as marked operation cycles. According to the sporadic high-load operations, the first pre-normal load operation in the marked operation cycle and the second pre-normal load operation in other operation cycles are searched in the work log, and operation data of the first pre-normal load operation and the operation data of the second pre-normal load operation are obtained. The operation data of the first pre-normal load operation is compared with the operation data of the second pre-normal load operation to obtain significant operation characteristics between the operation data. Based on the significant operation characteristics, the difference between the operation data of the first pre-normal load operation and the operation data of the second pre-normal load operation corresponding to the significant operation characteristics is used as a difference degree value, and the actual operation data of the target device when the sporadic high-load operation occurs is searched in the work log, and the predicted electric energy data of the target device is determined according to the difference degree value and the actual operation data, and subsequent operations are performed based on the predicted electric energy data. The embodiment of the present invention determines the sporadic high-load operation through the continuity of the high-load operation, thereby achieving accurate distinction of the sporadic high-load operation, extracts significant operating features based on the operating data of the first preceding normal load operation and the operating data of the second preceding normal load operation of the sporadic high-load operation, determines the difference degree value based on the significant operating features, and determines the predicted electric energy data of the target equipment according to the difference degree value, thereby achieving accurate prediction of the power load, ensuring the rationality of the power allocation strategy, and improving the safety of equipment operation.

[0062] Based on the above embodiments, the embodiments of the present invention further include:

[0063] Monitor the operating data of the target equipment; when the operating data shows significant operating characteristics, apply for electricity allocation according to the predicted power data.

[0064] Specifically, during the subsequent operation of the target equipment, the operating data of the target equipment is continuously monitored. When the monitored operating data shows significant operating characteristics, it indicates that the target equipment is about to enter a high-load operating state. At this time, the power allocation application is submitted in a timely manner based on the predicted power data to ensure the reasonable allocation and efficient use of power. By continuously monitoring the operating data of the target equipment, power resources can be allocated in advance to ensure that the equipment can obtain sufficient power supply when occasional high-load operations occur, avoiding equipment damage and production interruptions, and achieving the beneficial effect of improving the safety and stability of the production process.

[0065] Embodiment 2

[0066] Figure 2This is a flow chart of another method for predicting the adjustable potential of industrial power load provided by the second embodiment of the present invention. Based on the above embodiment, this embodiment of the present invention provides another method for predicting the adjustable potential of industrial power load. Figure 2 As shown, the method includes:

[0067] S201. Obtain a work log of a target device, and read high-load operations of different operation cycles in the work log.

[0068] Specifically, a work log of the target device is obtained from the target device, where the work log records the operating data of the target device in each operation cycle. According to the obtained work log of the target device, a search operation is performed in the work log to read out the high-load operation of the target device in different operation cycles.

[0069] For example, based on the above embodiments, the embodiments of the present invention can be applied to high-energy consumption and high-precision industrial fields that require precise monitoring and management of equipment loads. Among them, three typical application fields may include: chemical production, semiconductor manufacturing, and aerospace manufacturing. In the above three fields, the target equipment will experience significant load fluctuations at different process stages, and the stability of the target equipment is crucial to production efficiency and product quality. Load management and power allocation need to be carried out through historical data analysis and prediction. Chemical production can be selected as an application field. In the field of chemical production, because the power demand of the reactor changes dramatically at different reaction stages, any unstable load may affect the stability of the reaction and the quality of the product. Therefore, in order to ensure the stability of the power supply of the reactor, the reactor is used as the target equipment. It is crucial to maintain the safety of the reactor operation process and the consistency of the product. Obtain the work log of the reactor and find the high-load operation of the reactor in different operation cycles in the work log of the reactor.

[0070] S202: Identify the type of each high-load operation, and aggregate high-load operations of the same type to obtain a high-load operation set of the corresponding type.

[0071] In the embodiment of the present invention, the high-load operation set may be understood as a set formed by summarizing high-load operations of the same type, wherein the type of high-load operation may include: a startup operation type or a shutdown operation type.

[0072] Specifically, high-load operations of different operation cycles are obtained from the work log of the target device, and the high-load operations of different operation cycles are classified according to the type of high-load operation, and the high-load operations of the same type are aggregated to form a high-load operation set of the corresponding type. The high-load operation sets of the same type are aggregated into high-load operation sets of the corresponding type to facilitate subsequent unified analysis and processing.

[0073] S203 . For each high-load operation set, arrange each high-load operation in the high-load operation set in chronological order.

[0074] Specifically, each type of high-load operation is aggregated into each high-load operation set of the corresponding type. For each high-load operation set, the timestamp corresponding to each high-load operation is extracted from the work log of the target device. In each high-load operation set, the high-load operations of the same type are arranged in the order of occurrence of the timestamps. Arranging each high-load operation in chronological order is helpful for analyzing the continuity of each high-load operation.

[0075] S204 . For each high-load operation set, obtain timestamp differences of adjacent high-load operations in the high-load operation set, and determine the ratio of the number of differences with the same timestamp difference to the total number of all differences.

[0076] In the embodiment of the present invention, the timestamp difference may be understood as the time interval between two adjacent high-load operations, and is used to analyze the continuity of the high-load operations.

[0077] The number of differences can be understood as the number of identical time intervals between adjacent high-load operations, and is used to calculate the ratio of the number of differences to the total number of all differences.

[0078] Specifically, for each high-load operation set, the timestamp of each high-load operation is extracted in each high-load operation set, and based on the timestamp of each high-load operation, the time interval between adjacent high-load operations in each high-load operation set is calculated, and the time interval between adjacent high-load operations is defined as the timestamp difference between adjacent high-load operations. The number of differences with the same timestamp difference is obtained, the total number of all timestamp differences is obtained, and the number of differences with the same timestamp difference is compared with the total number of all timestamp differences to obtain the proportion of the number of differences with the same timestamp difference to the total number of all differences.

[0079] S205: When the ratio is less than a preset threshold, determine that all high-load operations in the high-load operation set corresponding to the ratio are not continuous.

[0080] In the embodiment of the present invention, the preset threshold value may be understood as a preset value used to determine the continuity of the high-load operation.

[0081] Specifically, a pre-set preset threshold is obtained, the ratio of the number of differences with the same timestamp difference to the total number of all differences is calculated, the ratio is compared with a preset threshold for determining the continuity of high-load operations, and a comparison result is obtained. When the comparison result is that the ratio is less than the preset threshold, all high-load operations in the high-load operation set corresponding to the ratio are determined to be incoherent, wherein the basis for setting the preset threshold may include: setting based on human experience or obtaining based on data analysis, and the embodiments of the present invention are not limited to this.

[0082] It can be understood that by analyzing the high-load operation time intervals of the equipment in different operating cycles, identifying the number of differences therein, and using the frequency of occurrence of these difference numbers as a measure of continuity, if the proportion of a certain time interval is higher than a preset threshold, the corresponding high-load operation is considered to have temporal continuity; otherwise, the high-load operation is considered to be an occasional high-load operation.

[0083] For example, in the field of chemical production, the preset threshold is set to 10 minutes. If the time intervals of high-load operation of the target equipment reactor in different operating cycles are concentrated within a fixed 10 minutes, then the high-load operation of the reactor is determined to be consistent. The high-load operation of the reactor with consistency can be predicted based on historical rules.

[0084] S206: Treat each incoherent high-load operation as an occasional high-load operation, and mark the operation cycle to which the occasional high-load operation belongs as a marked operation cycle.

[0085] Specifically, if the proportion of the number of differences with the same timestamp difference to the total number of all differences is less than a preset threshold, then it is determined that all high-load operations in the high-load operation set corresponding to the proportion are incoherent, the incoherent high-load operations are determined as sporadic high-load operations, and the operation cycle containing the sporadic high-load operations is marked as a marked operation cycle.

[0086] It is understandable that sporadic high-load operations lack temporal regularity, making high-load operations difficult to predict, and high-load operations are usually affected by external factors or randomness. Therefore, identifying sporadic operations is a key step in managing equipment load and optimizing power allocation, which helps to improve equipment operation stability and production efficiency.

[0087] S207: extracting the normal load operation before the occasional high load operation in the marked operation cycle from the work log, and taking the normal load operation as the first preceding normal load operation.

[0088] Specifically, based on the occasional high load operation, the normal load operation of the target device before the occasional high load operation occurs is extracted from the work log of the target device, and the normal load operation before the occasional high load operation is recorded as the first pre-normal load operation, wherein the occasional high load operation is included in the marked operation cycle, and the normal load operation can be understood as the operation that makes the target device operate within the normal range.

[0089] S208. Extracting a second pre-normal load operation of the same type as the first pre-normal load operation for other operation cycles except the marked operation cycle in the work log.

[0090] Specifically, the operating cycles other than the marked operating cycles that do not include occasional high-load operations are referred to as other operating cycles. In the work log of the target device, for other operating cycles of high-load operations, normal load operations of the same type as the first front-end normal load operation are extracted from the other operating cycles of high-load operations, and normal load operations of the same type as the first front-end normal load operation extracted from other operating cycles are defined as second front-end normal load operations.

[0091] For example, in the field of chemical production, if the target equipment is a reactor, if the most recent operation that put the reactor within the normal range before the work log of the reactor obtains the mark of the occasional high-load operation within the operating cycle is a adding operation, then the first pre-normal load operation of the reactor is the adding operation, and accordingly, the second pre-normal load operation of the reactor is the adding operation in other operating cycles, and the second pre-normal load operation is of the same type as the first pre-normal load operation.

[0092] It can be understood that if the first pre-normal load operation is the feeding operation of the reactor, in which the raw materials are injected into the reactor and begin to be heated, the corresponding occasional high load operation may be a feeding overload caused by abnormal raw material viscosity or temperature during the feeding process; the second pre-normal load operation is a feeding operation in another operating cycle, and the corresponding occasional high load operation may also be a feeding overload.

[0093] S209. Extracting operation data of the first front-end normal load operation and the second front-end normal load operation in the work log.

[0094] Specifically, the first pre-normal load operation and the second pre-normal load operation are extracted, and the operation data corresponding to the first pre-normal load operation and the operation data corresponding to the second pre-normal load operation are searched in the work log.

[0095] For example, in the field of chemical production, if the target equipment is a reactor, the operating data of the reactor may include: feeding speed (indicating the rate of raw material injection), internal temperature of the reactor (temperature change during the feeding process), power consumption (actual energy consumption of the reactor during the feeding process) and pressure change (change in pressure inside the reactor during the feeding process).

[0096] S210: Determine feature differences of all identical features in the operating data of the first pre-normal load operation and the second pre-normal load operation.

[0097] In the embodiment of the present invention, the characteristic difference may be understood as an offset of each load operation on the operating data, wherein each load operation at least includes: a first pre-normal load operation and a second pre-normal load operation.

[0098] Specifically, the features in the first pre-normal load operation running data and the features in the second pre-normal load operation running data are obtained, all the same features in the first pre-normal load operation and the second pre-normal load operation running data are compared to obtain the feature offset, and the feature offset is defined as the feature difference of the running data.

[0099] For example, the characteristics within the operating data of the first pre-normal load operation and the second pre-normal load operation may include: operating speed or operating temperature. The operating speeds of the first pre-normal load operation and the second pre-normal load operation are set to 300 revolutions per minute and 200 revolutions per minute, respectively, and the operating temperatures are set to 50 degrees Celsius and 30 degrees Celsius, respectively. Comparing the operating speeds, the characteristic difference is 100 revolutions per minute, and comparing the operating temperatures, the characteristic difference is 20 degrees Celsius.

[0100] S211. When the characteristic difference is greater than the characteristic threshold, the characteristic of the operating data of the first front-end normal load operation is taken as a significant operating characteristic.

[0101] In the embodiment of the present invention, the feature threshold may be understood as a preset value used to identify significant operating features.

[0102] Specifically, a characteristic threshold is obtained, the same characteristics in the operating data of the first pre-normal load operation and the second pre-normal load operation are compared to obtain a characteristic difference, the characteristic difference is compared with the characteristic threshold to obtain a comparison result, wherein when the comparison result is that the characteristic difference is less than the characteristic threshold, the characteristic corresponding to the characteristic difference in the operating data of the first pre-normal load operation is used as a significant operating characteristic of the target device, wherein the characteristic threshold setting basis may include: setting based on human experience or obtaining based on data analysis, which is not limited in the embodiments of the present invention.

[0103] For example, the operating speed characteristic threshold is set to 80 revolutions per minute, and the operating temperature characteristic threshold is set to 30 degrees Celsius. The operating speed characteristic difference and the operating temperature characteristic difference are compared with the operating speed characteristic threshold and the operating temperature characteristic threshold respectively. If the operating speed characteristic difference is greater than the operating speed characteristic threshold, and the operating temperature characteristic difference is less than the operating temperature characteristic threshold, then the operating speed is taken as a significant operating feature.

[0104] For example, in the field of chemical production, the first pre-normal load operation and the second pre-normal load operation are both feeding processes. When comparing the equipment operation data of these two operations, it is found that the feeding speed, internal temperature rise rate and power consumption in the first pre-normal load operation all have different degrees of deviation. Through comparison, the feeding speed with the largest deviation can be selected as the significant operating feature of the target equipment.

[0105] S212: For the significant operation characteristics, the difference between the operation characteristics of the first front-end normal load operation and the characteristics of the second front-end normal load operation is used as a difference degree value.

[0106] In the embodiment of the present invention, the characteristic difference may be understood as an offset between the operating characteristics, and is used to quantify the degree of difference in operating data between the first pre-normal load operation and the second pre-normal load operation.

[0107] The operation characteristic may be understood as a data indicator included in the operation data. For example, the operation characteristic may include: a pressure value of the target device or a power consumption of the target device.

[0108] Specifically, for the significant operating characteristics, the operating characteristics corresponding to the significant operating characteristics are searched in the operating data of the first pre-normal load operation, and the operating characteristics corresponding to the significant operating characteristics are searched in the operating data of the second pre-normal load operation, and the characteristic difference between the operating characteristics of the first pre-normal load operation and the operating characteristics of the second pre-normal load operation is used as the difference degree value.

[0109] S213. Extracting actual operation data of occasional high-load operations in the work log.

[0110] Specifically, the occasional high-load operation is determined based on the continuity of the high-load operation, and the actual operation data of the target device when the occasional high-load operation occurs is extracted from the work log of the target device.

[0111] S214. Adjust the actual operation data according to the difference degree value to obtain predicted electric energy data.

[0112] Specifically, actual operating data is obtained when sporadic high-load operations occur, and the actual operating data is adjusted according to the difference value determined between the operating characteristics of the first pre-normal load operation and the operating characteristics of the second pre-normal load operation, and the electric energy data required to be adjusted for the adjusted target device is used as the predicted electric energy data for the target device.

[0113] For example, the feeding speed of the reactor is taken as a significant operating feature, the feeding speed data in the first pre-normal load operation is extracted, and compared with the feeding speed data in the second pre-normal load operation, and the difference degree value of the two sets of data is calculated. For example, the feeding speed of the first pre-normal load operation deviates by 25% from the feeding speed of the second pre-normal load operation, then 25% is taken as the difference degree value, and an adjustment amount of 25% is added to the actual operating data to obtain the predicted power data. The calculation of the difference degree value ensures that the occasional high-load operation of the equipment in the first pre-normal load operation is accurately identified. By calculating the predicted power data, appropriate power support can be allocated in advance for possible occasional high-load operations, ensuring that the target equipment can obtain sufficient power supply in the case of occasional high loads, avoiding equipment failures or production interruptions due to insufficient power.

[0114] In an embodiment of the present invention, high-load operations of a target device in different operation cycles are searched from a work log of the target device, and the high-load operations in different operation cycles are classified according to the type of the high-load operations to obtain a high-load operation set of the corresponding type. In each high-load operation set, each high-load operation is arranged according to the order in which the timestamps of each high-load operation occur, and the timestamp differences between adjacent high-load operations in each high-load operation set are identified. The number of differences with the same timestamp difference and the total number of all timestamp differences are calculated, and the number of differences with the same timestamp difference and the total number of all timestamp differences are compared to obtain the ratio of the number of differences with the same timestamp difference to the total number of all differences. The obtained ratio is compared with a preset threshold to obtain a comparison result. When the comparison result is that the ratio is less than the preset threshold, it is determined that all high-load operations in the high-load operation set corresponding to the ratio are incoherent, and the incoherent high-load operations are determined as sporadic high-load operations. The operation cycle containing the sporadic high-load operations is marked as a marked operation cycle. High-load operation, extract the first pre-normal load operation of the target device before the occurrence of occasional high-load operation in the work log, and extract the second pre-normal load operation of the same type as the first pre-normal load operation in other operation cycles, obtain the operation data of the first pre-normal load operation and the operation data of the second pre-normal load operation, compare all the same features in the operation data of the first pre-normal load operation and the second pre-normal load operation, obtain the feature difference, compare the feature difference with the feature threshold, and obtain a comparison result. When the comparison result is that the feature difference is greater than the feature threshold, the corresponding feature in the operation data of the first pre-normal load operation is used as the significant operation feature of the target device. According to the significant operation feature, the feature difference between the operation data of the first pre-normal load operation and the operation data of the second pre-normal load operation is used as the difference degree value. The actual operation data of the target device when the occasional high-load operation occurs is extracted from the work log of the target device, and the actual operation data is adjusted according to the difference degree value to obtain predicted electric energy data.The embodiment of the present invention aggregates high-load operation sets of the same type into a high-load operation set, and performs subsequent operations in the high-load operation set, which is convenient for unified processing and analysis of high-load operations, and improves data processing efficiency and accuracy; arranging each high-load operation in chronological order is helpful to analyze the time distribution law of each high-load operation, and then effectively judge the continuity of each high-load operation; by setting a preset threshold, the ratio is compared with the preset threshold to judge whether the high-load operation is an occasional high-load operation, thereby achieving accurate distinction of occasional high-load operations; by setting a characteristic threshold, the characteristic difference between the operating data of the first front-end normal load operation and the operating data of the second front-end normal load operation is compared with the significant operating characteristic to obtain a difference degree value, and the actual operating data is adjusted according to the difference degree value, thereby achieving accurate prediction of power load, ensuring the rationality of the power allocation strategy, and improving the safety of equipment operation.

[0115] Embodiment 3

[0116] Figure 3 A system for predicting the adjustable potential of industrial power load provided in the third embodiment of the present invention is as follows: Figure 3 As shown, the system includes:

[0117] The high-load operation extraction module 301 is used to obtain the work log of the target device and determine the high-load operations of the target device in different operation cycles according to the work log;

[0118] A continuity determination module 302, for determining occasional high-load operations and marking operation cycles according to the continuity of high-load operations in different operation cycles;

[0119] An operation extraction module 303 is used to obtain the first preceding normal load operation of the marked operation cycle and the second preceding normal load operation of other operation cycles in the work log according to the occasional high load operation;

[0120] A difference determination module 304 is used to compare the operation data of the first front normal load operation and the second front normal load operation to obtain a significant operation feature;

[0121] The power prediction module 305 is used to determine the difference degree value corresponding to the significant operation feature, and determine the predicted power data based on the difference degree value and the actual operation data corresponding to the occasional high-load operation in the work log.

[0122] According to an embodiment of the present invention, a work log of a target device is obtained from the target device, and high-load operations of the target device in different operation cycles are searched in the work log, the continuity of high-load operations in different operation cycles is judged, and high-load operations without continuity are determined as sporadic high-load operations, and operation cycles containing sporadic high-load operations are determined as marked operation cycles. According to the sporadic high-load operations, the first pre-normal load operation in the marked operation cycle and the second pre-normal load operation in other operation cycles are searched in the work log, and operation data of the first pre-normal load operation and the operation data of the second pre-normal load operation are obtained. The operation data of the first pre-normal load operation is compared with the operation data of the second pre-normal load operation to obtain significant operation characteristics between the operation data. Based on the significant operation characteristics, the difference between the operation data of the first pre-normal load operation and the operation data of the second pre-normal load operation corresponding to the significant operation characteristics is used as a difference degree value, and the actual operation data of the target device when the sporadic high-load operation occurs is searched in the work log, and the predicted electric energy data of the target device is determined according to the difference degree value and the actual operation data, and subsequent operations are performed based on the predicted electric energy data. The embodiment of the present invention determines the sporadic high-load operation through the continuity of the high-load operation, thereby achieving accurate distinction of the sporadic high-load operation, extracts significant operating features based on the operating data of the first preceding normal load operation and the operating data of the second preceding normal load operation of the sporadic high-load operation, determines the difference degree value based on the significant operating features, and determines the predicted electric energy data of the target equipment according to the difference degree value, thereby achieving accurate prediction of the power load, ensuring the rationality of the power allocation strategy, and improving the safety of equipment operation.

[0123] Based on the above embodiments, the embodiments of the present invention further include:

[0124] Monitor the operating data of the target equipment; when the operating data shows significant operating characteristics, apply for electricity allocation according to the predicted power data.

[0125] Based on the above embodiments, Figure 4 As shown, the high-load operation extraction module 301 also includes: a log search unit 3010, which is used to obtain the work log of the target device and read the high-load operations of different operation cycles in the work log; a high-load operation set acquisition unit 3011, which is used to identify the type of each high-load operation, and aggregate the high-load operations of the same type to obtain a high-load operation set of the corresponding type; a timeline sorting unit 3012, which is used to arrange the high-load operations in each high-load operation set in chronological order.

[0126] Based on the above embodiments, Figure 5As shown, the consistency judgment module 302 also includes: a high-load operation set analysis unit 3020, which is used to obtain the timestamp differences of adjacent high-load operations in each high-load operation set, and determine the ratio of the number of differences with the same timestamp differences to the total number of all differences; an occasional high-load operation determination unit 3021, which is used to determine that all high-load operations in the high-load operation set corresponding to the ratio are incoherent when the ratio is less than a preset threshold; a marking operation cycle setting unit 3022, which is used to treat each incoherent high-load operation as an occasional high-load operation, and to mark the operation cycle to which the occasional high-load operation belongs as a marked operation cycle.

[0127] Based on the above embodiments, Figure 6 As shown, the operation extraction module 303 also includes: a first pre-normal load operation determination unit 3030, which is used to extract the normal load operation before the occasional high load operation in the marked operation cycle in the work log, and use the normal load operation as the first pre-normal load operation; a second pre-normal load operation determination unit 3031, which is used to extract the second pre-normal load operation of the same type as the first pre-normal load operation for other operation cycles except the marked operation cycle in the work log.

[0128] Based on the above embodiments, Figure 7 As shown, the difference determination module 304 also includes: an operation data extraction unit 3040, which is used to extract the operation data of the first front-end normal load operation and the second front-end normal load operation in the work log; a feature difference determination unit 3041, which is used to determine the feature difference of all the same features in the operation data of the first front-end normal load operation and the second front-end normal load operation; a significant operation feature determination unit 3042, which is used to use the feature of the operation data of the first front-end normal load operation as a significant operation feature when the feature difference is greater than the feature threshold.

[0129] Based on the above embodiments, Figure 8 As shown, the electric energy prediction module 305 also includes: a difference degree value determination unit 3050, which is used to use the characteristic difference between the operating characteristics of the first pre-normal load operation and the operating characteristics of the second pre-normal load operation as the difference degree value for the significant operating characteristics; an actual operation number extraction unit 3051, which is used to extract the actual operating data of occasional high-load operations in the work log; and a predicted electric energy data acquisition unit 3052, which is used to adjust the actual operating data according to the difference degree value to obtain predicted electric energy data.

[0130] Embodiment 4

[0131] Embodiments of the present invention provide a device for executing a method for predicting the adjustable potential of industrial power load, a computer-readable storage medium, and a computer program product.

[0132] Fig. 9 A schematic diagram of a structure of an apparatus that can be used to implement an embodiment of the present invention is shown. The apparatus is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The apparatus may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown in the embodiments of the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present invention described and / or required herein.

[0133] like Fig. 9 As shown, the device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the ROM 12 or the computer program loaded from the storage unit 18 to the RAM 13. In the RAM 13, various programs and data required for the operation of the device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0134] A number of components in the device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0135] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit, a graphics processing unit, various special artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, for example, a method for predicting the adjustable potential of industrial power loads.

[0136] In some embodiments, the method for predicting the adjustable potential of industrial power loads may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for predicting the adjustable potential of industrial power loads may be performed. Alternatively, in other embodiments, the processor 11 may be configured as a method for predicting the adjustable potential of industrial power loads in any other appropriate manner (e.g., by means of firmware).

[0137] Various implementations of the systems and techniques described above in the embodiments of the present invention may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays, application specific integrated circuits, application specific standard products, systems on a chip, load programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: being implemented in one or more computer programs, which may be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general programmable processor, which may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0139] In the context of an embodiment of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device (e.g., a cathode ray tube or a liquid crystal display monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including: acoustic input, voice input, or tactile input).

[0141] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area networks, wide area networks, blockchain networks, and the Internet.

[0142] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private server services.

[0143] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0144] The above specific implementations do not constitute a limitation on the protection scope of the embodiments of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the adjustable potential of industrial power load, characterized in that: The method comprises: Obtaining a work log of a target device, and determining high-load operations of the target device in different operation cycles according to the work log; Determining occasional high-load operations and marking operation cycles according to the continuity of the high-load operations in different operation cycles; Acquire the first preceding normal load operation of the marked operation cycle and the second preceding normal load operation of the other operation cycles in the work log according to the occasional high load operation; Comparing the operation data of the first front-end normal load operation and the second front-end normal load operation to obtain significant operation characteristics; Determine a difference degree value corresponding to the significant operating feature, and determine predicted power data based on the difference degree value and actual operating data corresponding to the occasional high-load operation in the work log.

2. The method according to claim 1, characterized in that: The obtaining of the work log of the target device and determining the high-load operation of the target device in different operation cycles according to the work log include: Acquire the work log of the target device, and read the high-load operations of the different operation cycles in the work log; Identifying the type of each of the high-load operations, and aggregating the high-load operations of the same type to obtain a high-load operation set corresponding to the type; For each of the high-load operation sets, the high-load operations in the high-load operation set are arranged in chronological order.

3. The method according to claim 2, characterized in that: The determining of the occasional high-load operation and marking the operation cycle according to the continuity of the high-load operation in different operation cycles includes: For each of the high-load operation sets, obtaining timestamp differences of adjacent high-load operations in the high-load operation set, and determining the proportion of the number of differences with the same timestamp difference to the total number of all differences; When the ratio is less than a preset threshold, determining that all the high-load operations in the high-load operation set corresponding to the ratio are not continuous; Each of the high-load operations that are not consistent is regarded as the occasional high-load operation, and the operation cycle to which the occasional high-load operation belongs is marked as the marked operation cycle.

4. The method according to claim 1, characterized in that: The step of obtaining the first preceding normal load operation of the marked operation cycle and the second preceding normal load operation of other operation cycles in the work log according to the occasional high load operation includes: Extracting the normal load operation before the occasional high load operation in the marked operation cycle from the work log, and using the normal load operation as the first pre-normal load operation; A second pre-normal load operation of the same type as the first pre-normal load operation is extracted from the work log for other operation cycles except the marked operation cycle.

5. The method according to claim 1, characterized in that: The comparing the operation data of the first front normal load operation and the second front normal load operation to obtain significant operation characteristics includes: extracting the operation data of the first front normal load operation and the second front normal load operation in the work log; determining characteristic differences of all identical characteristics in the operating data of the first pre-normal load operation and the second pre-normal load operation; When the characteristic difference is greater than a characteristic threshold, the characteristic of the operating data of the first front-end normal load operation is used as the significant operating characteristic.

6. The method according to claim 1, characterized in that: The determining of the difference degree value corresponding to the significant operating feature, and determining the predicted power data based on the difference degree value and the actual operating data corresponding to the occasional high-load operation in the work log, includes: For the significant operating characteristic, taking the characteristic difference between the operating characteristic of the first front-end normal load operation and the operating characteristic of the second front-end normal load operation as the difference degree value; Extracting actual operation data of the occasional high-load operation in the work log; The actual operation data is adjusted according to the difference degree value to obtain the predicted electric energy data.

7. The method according to claim 1, characterized in that: Also includes: Monitoring the operating data of the target device; When the significant operating characteristics appear in the operating data, an application for electricity allocation is made according to the predicted electric energy data.

8. The industrial power load adjustable potential prediction system is characterized by: The system comprises: A high-load operation extraction module, used to obtain a work log of a target device, and determine the high-load operations of the target device in different operation cycles according to the work log; A continuity judgment module, used to determine occasional high-load operations and mark operation cycles according to the continuity of the high-load operations in different operation cycles; An operation extraction module, configured to obtain, in the work log, a first preceding normal load operation of the marked operation cycle and a second preceding normal load operation of other operation cycles according to the occasional high load operation; a difference determination module, configured to compare the operation data of the first front normal load operation and the second front normal load operation to obtain a significant operation feature; The electric energy prediction module is used to determine the difference degree value corresponding to the significant operating characteristics, and determine the predicted electric energy data based on the difference degree value and the actual operating data corresponding to the occasional high-load operation in the work log.

9. A device, characterized in that: The device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting the adjustable potential of industrial electricity load according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting the adjustable potential of industrial power load according to any one of claims 1 to 7 when executed.

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