A load-adaptive charge management system for industrial chargers

Abstract

The application discloses a load-adaptive charging management system of an industrial charger and relates to the technical field of industrial energy management, which improves the operation stability of industrial equipment. According to a plurality of equipment state data of each industrial equipment, a plurality of confidence value curves and an estimated energy demand curve of each industrial equipment under an operation supervision period are analyzed, an energy allocation request is generated according to the estimated energy demand curve and the confidence value curve of each industrial equipment and is sent to a power supervision end, a load evaluation pool and an energy allocation decision pool are set, the energy allocation request of the industrial equipment is input into the energy allocation decision pool, and then energy allocation decisions of the industrial charger are set and executed by matching the load evaluation pool and the energy allocation decision pool with each other.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial energy management, and particularly relates to a load adaptive charging management system of an industrial charger. BACKGROUND

[0002] In the field of industrial production, the stable operation of industrial equipment and the reasonable allocation of energy have always been crucial issues. Traditional industrial equipment energy management methods often lack precision and dynamic adaptability.

[0003] In the past, the collection of industrial equipment state data lacks systematicness and periodic planning, making it difficult to comprehensively and accurately grasp the operation status of industrial equipment at different time periods. This leads to the inability to deeply analyze the operation characteristics of industrial equipment, such as the difficulty in determining the multiple confidence value curves and the estimated energy demand curve of the equipment during operation. Due to the inability to accurately estimate energy demand, unreasonable situations are prone to occur in energy allocation, either energy supply is insufficient, affecting the normal operation of industrial equipment and reducing production efficiency, or energy supply is excessive, causing energy waste and increasing production costs.

[0004] At the same time, in terms of power management, traditional methods do not fully consider real-time energy storage and load power information, and cannot establish a scientific and reasonable load assessment pool and energy allocation decision pool for industrial chargers and industrial equipment respectively. This makes it difficult to effectively match and make decisions when facing energy allocation requests of industrial equipment, resulting in low efficiency of energy allocation and inability to achieve optimal allocation of energy. Therefore, a load adaptive charging management system of an industrial charger is provided. SUMMARY

[0005] In order to solve the above technical problems, the purpose of the present application is to provide a load adaptive charging management system of an industrial charger.

[0006] In order to achieve the above purpose, the present application provides the following technical solutions:

[0007] A load adaptive charging management system of an industrial charger, comprising a load supervision end and a power supervision end;

[0008] The load supervision end is used to set an operation supervision period, and then collect multiple equipment state data of each industrial equipment in several operation supervision periods, and analyze multiple confidence value curves and estimated energy demand curves of each industrial equipment in the operation supervision period according to the multiple equipment state data of each industrial equipment;

[0009] Then, according to the estimated energy demand curve and the confidence value curve of each industrial equipment, an energy allocation request is generated and sent to the power supervision end;

[0010] The power supervision end is used for setting a load evaluation pool for each industrial charger according to real-time energy storage amount and load power information, setting an energy allocation decision pool for each industrial device, inputting an energy allocation request of the industrial device into the energy allocation decision pool, and then setting an energy allocation decision for each industrial charger and executing by matching the load evaluation pool and the energy allocation decision pool.

[0011] Further, the collection process of the device state data includes:

[0012] Type information and production demand of each industrial device are obtained, and then different operation supervision periods are set for each industrial device according to the type information and the production demand;

[0013] A plurality of sensors are installed on each industrial device, and a number is set, and each time the operation supervision period of an industrial device ends, the sensors collect device state data of the industrial device.

[0014] Further, the acquisition process of the confidence value curve and the estimated energy demand curve includes:

[0015] n operation time axes are established, a plurality of sub-time segments are divided on the operation time axes according to time lengths of the operation supervision periods of each industrial device, and n is a natural number greater than 0;

[0016] A two-dimensional coordinate system is established, device state data corresponding to the same industrial device and of the same data type and in the sub-time segments are mapped on the same two-dimensional coordinate system, and a plurality of time nodes are set in the sub-time segments;

[0017] A plurality of numerical intervals are set, the occurrence times of device state data segments between each time node in each numerical interval are counted, and then the numerical intervals of each time node are set with a confidence according to the occurrence times;

[0018] A confidence value is acquired according to the confidence of each numerical interval between each time node, and the calculation formula of the confidence is:

[0019]

[0020] Wherein, β i，j represents the confidence of the jth numerical interval between the ith pair of time nodes, N j represents the time occurrence times of the device state data segments of the jth numerical interval, and Num irepresents the total time occurrence number of the device state data segment between the ith pair of time nodes, p represents the time occurrence number, represents the time distance between the segments in the device state data collected by the running data collection module from the current time node, p = 0, 1, …, i, j are natural numbers greater than 0;

[0021] The confidence values between each time node are sequentially spliced according to the time node sequence to obtain each confidence value curve of each sub-time section, and then the confidence value curves corresponding to the voltage and the current are used to obtain the estimated energy demand curve of the corresponding sub-time section, and the estimated energy demand curve and each confidence value curve are marked on the running time axis.

[0022] Further, the generation process of the energy deployment request comprises:

[0023] Whenever a running supervision period ends, the corresponding confidence value curve and the estimated energy demand curve are called on the running time axis according to the corresponding time sequence of the real-time device state data;

[0024] The running deviation threshold and the parameter deviation threshold are set, and a plurality of running deviation correction parameters are set;

[0025] The device state data is divided into running state data and parameter state data according to the category, and if it is judged that the difference between the real-time parameter state data and the corresponding confidence value curve at any time node is less than or equal to the parameter deviation threshold, the real-time parameter state data segment on the corresponding time node is retained;

[0026] If it is judged that the difference between the real-time parameter state data and the corresponding confidence value curve at any time node is greater than the parameter deviation threshold, the multiplication result of the difference and the confidence degree of the real-time parameter state data in the corresponding time node is added to the corresponding real-time parameter state data segment.

[0027] Further, the generation process of the energy deployment request further comprises:

[0028] If it is judged that the difference between the real-time running state data and the corresponding confidence value curve at any time node is less than or equal to the running deviation threshold, no operation is performed, otherwise, according to the confidence degree of the real-time running state data in the corresponding time node, the multiplication result of the difference, the confidence degree and the running deviation correction parameter is used to reduce each real-time running state data segment between the corresponding time nodes;

[0029] According to each real-time running state data, a predicted energy demand at each time node in the next running supervision period is generated, and the predicted energy demand between each time node is mapped on the estimated energy demand curve to generate an energy demand interval between each time node;

[0030] According to the energy demand interval between each time node and the industrial equipment number, an energy allocation request is generated and sent to the power supervision end.

[0031] Further, the setting process of the load evaluation pool and the energy allocation decision pool includes:

[0032] The number of each industrial charger is obtained, and the maximum load power is obtained. When a running supervision period starts, the power supervision end obtains the real-time energy storage amount and the real-time load power of each industrial charger, and obtains the idle load power of the corresponding industrial charger according to the real-time load power and the maximum load power;

[0033] A load evaluation pool is set for each industrial charger, and the real-time energy storage amount, the idle load power and the number of the industrial charger are marked in the load evaluation pool;

[0034] And an energy allocation decision pool is set for each industrial equipment, and the quota running power, the number and the energy allocation request of the industrial equipment are marked in the energy allocation decision pool.

[0035] Further, the mutual matching process of the load evaluation pool and the energy allocation decision pool includes:

[0036] When a running detection period starts, the total energy demand is generated according to the energy demand interval in the energy allocation request of each energy allocation decision pool, the demand priority score is set according to the size of the total energy demand, and the maximum energy supply amount of the next running supervision period is obtained according to the idle load power and the real-time energy storage amount of the load evaluation pool, and the supply priority score is set according to the size of the maximum energy supply amount;

[0037] According to the order of the size of the demand priority score and the supply priority score, each energy allocation decision pool and load evaluation pool are matched with each other. If the maximum energy supply amount of the load evaluation pool is greater than or equal to the total energy demand of the energy allocation decision pool, an energy allocation decision is generated for the load evaluation pool according to the total energy demand of the energy allocation decision pool;

[0038] Otherwise, the total energy demand of the energy allocation decision pool is subtracted by the maximum energy supply amount of the load evaluation pool, and the priority score of the corresponding energy allocation decision pool is updated according to the difference.

[0039] Further, when the maximum energy supply of the entire load evaluation pool is allocated, or the entire energy allocation request is completed, the entire energy allocation decision is sent to the corresponding industrial charger and executed.

[0040] Compared with the prior art, the present application has the following advantages:

[0041] According to the real-time energy storage and load power information, the present application sets a load evaluation pool for each industrial charger and an energy allocation decision pool for each industrial device. The energy allocation request of the industrial device is input into the energy allocation decision pool, and the energy allocation decision for each industrial charger is realized by matching the load evaluation pool and the energy allocation decision pool.

[0042] Meanwhile, the matching method based on multiple factors fully considers the actual situation of the power supply and the energy demand of the industrial device, realizes the optimal allocation of energy, improves the energy utilization efficiency, reduces energy waste, and guarantees the stable operation of the industrial device and improves the production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The system flowchart of the present application is shown. DETAILED DESCRIPTION

[0044] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purpose, the specific embodiments, structures, features and effects of the present application are described in detail below in combination with the drawings and preferred embodiments.

[0045] As shown in Figure 1 , a load adaptation charging management system of an industrial charger includes a load supervision end and a power supply supervision end;

[0046] The load supervision end is used to set a running supervision period, collect a plurality of device state data of each industrial device in a plurality of running supervision periods, and analyze a plurality of confidence value curves and an estimated energy demand curve of each industrial device in the running supervision period according to the plurality of device state data of each industrial device.

[0047] Further, the energy allocation request is sent to the power supply supervision end according to the estimated energy demand curve and the confidence value curve of each industrial device.

[0048] The power supply supervision end is used to set a load evaluation pool for each industrial charger and an energy allocation decision pool for each industrial device according to the real-time energy storage and load power information, input the energy allocation request of the industrial device into the energy allocation decision pool, and set the energy allocation decision for each industrial charger by matching the load evaluation pool and the energy allocation decision pool and execute it.

[0049] Further, the working principle of the present application is illustrated by the following examples:

[0050] The load supervision end is provided with an operation data acquisition module, an operation analysis module and an energy demand module;

[0051] The operation data acquisition module is used to communicate with each industrial equipment in the application scene, thereby obtaining the type information and production demand of each industrial equipment, and then setting different operation supervision periods for each industrial equipment according to the type information and production demand;

[0052] For example, for high-precision processing equipment (such as numerical control machine tools), the time length of the operation supervision period is set to 10 seconds, and for heavy equipment (such as furnaces), the time length of the operation supervision period is set to 1 hour;

[0053] A variety of sensors, such as temperature sensors, voltage / current sensors, etc., are installed on each industrial equipment, and each sensor is in communication connection with the operation data acquisition module, and at the same time, the operation data acquisition module sets the numbers a1, a2, …, an for each industrial equipment; n n is a natural number greater than 0, and represents the total number of industrial equipment in the application scene;

[0054] Whenever the operation supervision period of an industrial equipment ends, the sensors on the corresponding industrial equipment upload the collected device state data to the operation data acquisition module.

[0055] Further, the operation data acquisition module labels the industrial equipment numbers on each device state data, and then synchronizes all device state data to the operation analysis module;

[0056] The operation analysis module is used to analyze a plurality of confidence value curves and an estimated energy demand curve of each industrial equipment under the operation supervision period according to a plurality of device state data of each industrial equipment, and the specific process includes:

[0057] Four operation quarters are set in years as the time unit, n operation time axes are established, and four time segments are divided on the operation time axes, and then each device state data is mapped above the corresponding time segment of the operation time axis in time sequence according to the operation quarter corresponding to the collection time of the device state data;

[0058] According to the time length of the operation supervision period of each industrial equipment, a plurality of sub-time segments are divided on the operation time axis;

[0059] A two-dimensional coordinate system is established, and the data of the same industrial equipment and the same type of data and the equipment state data in the sub-time section are mapped on the same two-dimensional coordinate system, and a plurality of time nodes are set in the sub-time section;

[0060] A plurality of numerical intervals are set, the occurrence times of the equipment state data segments between each pair of time nodes in each pair of numerical intervals are counted, and then the confidence of each pair of time nodes in each numerical interval is set according to the occurrence times, and the calculation formula of the confidence is:

[0061]

[0062] Wherein β i，j represents the confidence of the jth numerical interval between the ith pair of time nodes, N j represents the time occurrence of the equipment state data segment in the jth numerical interval, Num i represents the total time occurrence of the equipment state data segment between the ith pair of time nodes, p represents the time occurrence, p=0,1,……,i,j are natural numbers greater than 0;

[0063] According to the confidence of each numerical interval between each time node, the confidence value a between each time node is obtained, and the confidence value a=q1*β i，1 +……+q j *β i，j , q j represents the middle value of the jth numerical interval;

[0064] The confidence values a between each time node are sequentially spliced according to the time node order to obtain each confidence value curve of each sub-time section, and then the confidence value curves corresponding to the voltage and the current are obtained to obtain the estimated energy demand curve of the corresponding sub-time section, and the estimated energy demand curve and each confidence value curve are marked on the running time axis;

[0065] It should be noted that when a running supervision period ends, the running analysis module updates the estimated energy demand curve and each confidence value curve on the running time axis, and synchronizes the updated running time axis to the energy demand module.

[0066] Further, the energy demand module is used to generate an energy deployment request according to the estimated energy demand curve of each industrial equipment and the confidence value curve and send it to the power supervision end, and the specific process includes:

[0067] Whenever a running supervision period ends, the energy demand module retrieves the corresponding confidence value curve and the estimated energy demand curve on the running time axis according to the time sequence of the real-time device state data;

[0068] The running deviation threshold and the parameter deviation threshold are set, and a plurality of running deviation correction parameters are set;

[0069] The device state data is divided into running state data and parameter state data according to categories, wherein the running state data includes running temperature, running pressure, etc., and the parameter state data includes running voltage, running current, etc.;

[0070] If it is judged that the difference between the real-time parameter state data and the corresponding confidence value curve at any time node is less than or equal to the parameter deviation threshold, the real-time parameter state data segment at the corresponding time node is retained;

[0071] If it is judged that the difference between the real-time parameter state data and the corresponding confidence value curve at any time node is greater than the parameter deviation threshold, the multiplication result of the difference and the confidence degree of the numerical section in which the real-time parameter state data is located between the corresponding time nodes is added to the corresponding real-time parameter state data segment according to the confidence degree;

[0072] If it is judged that the difference between the real-time running state data and the corresponding confidence value curve at any time node is less than or equal to the running deviation threshold, no operation is performed, otherwise the real-time running state data segments between the corresponding time nodes are reduced by the multiplication result of the difference, the confidence degree and the running deviation correction parameter according to the confidence degree of the numerical section in which the real-time running state data is located between the corresponding time nodes;

[0073] The predicted energy demand at each time node in the next running supervision period is generated according to each real-time running state data, and the predicted energy demand between each time node is mapped on the estimated energy demand curve, thereby generating the energy demand interval between each time node;

[0074] The energy allocation request is generated according to the energy demand interval between each time node and the industrial device number and sent to the power supervision end.

[0075] Further, the power supervision end obtains each industrial charger setting number and obtains the maximum load power, wherein the numbers are b1, b2, …, b e , e is a natural number greater than 0;

[0076] And whenever a running supervision period starts, the power supervision end obtains the real-time energy storage amount and the real-time load power of each industrial charger, and obtains the idle load power of the corresponding industrial charger according to the real-time load power and the maximum load power;

[0077] a load evaluation pool is set for each industrial charger, and the real-time energy storage amount, idle load power and number of the industrial charger are marked in the load evaluation pool;

[0078] and an energy allocation decision pool is set for each industrial device, and the rated running power, number and energy allocation request of the industrial device are marked in the energy allocation decision pool;

[0079] When a running detection cycle starts, the total energy demand is generated according to the energy demand range in the energy allocation request of each energy allocation decision pool, the demand priority score is set according to the size of the total energy demand, the maximum energy supply amount of the next running supervision cycle is obtained according to the idle load power and real-time energy storage amount of the load evaluation pool, and the supply priority score is set according to the size of the maximum energy supply amount;

[0080] According to the order of the size of the demand priority score and the supply priority score, each energy allocation decision pool and load evaluation pool are matched with each other, if the maximum energy supply amount of the load evaluation pool is greater than or equal to the total energy demand of the energy allocation decision pool, an energy allocation decision is generated for the load evaluation pool according to the total energy demand of the energy allocation decision pool;

[0081] Otherwise, the total energy demand of the energy allocation decision pool is subtracted by the maximum energy supply amount of the load evaluation pool, and the priority score of the corresponding energy allocation decision pool is updated according to the difference, wherein the update formula of the priority score is: priority score=(difference / total energy demand)*priority score;

[0082] The above matching operation is repeated until the maximum energy supply amount of all load evaluation pools is allocated or all energy allocation requests are completed, and then all energy allocation decisions are sent to the corresponding industrial charger and executed.

[0083] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the technical solution of the present application. Any indirect modification, equivalent change and modification of the above embodiments according to the technical essence of the present application, which does not depart from the technical solution of the present application, are still within the scope of the technical solution of the present application.

Claims

1. A load-adaptive charge management system for an industrial charger, characterized by, The load monitoring end and the power monitoring end are included; The load monitoring end is used for setting an operation monitoring period, collecting a plurality of device state data of each industrial equipment in a plurality of operation monitoring periods, and analyzing a plurality of confidence value curves of each industrial equipment in the operation monitoring period and an estimated energy demand curve according to the plurality of device state data of each industrial equipment; The estimated energy demand curve and the confidence value curve of each industrial equipment are used to generate an energy deployment request and send the energy deployment request to the power monitoring end; The power monitoring end is used for setting a load evaluation pool for each industrial charger according to real-time energy storage and load power information, setting an energy deployment decision pool for each industrial equipment, inputting the energy deployment request of the industrial equipment into the energy deployment decision pool, and setting an energy deployment decision for each industrial charger and executing the energy deployment decision by matching the load evaluation pool and the energy deployment decision pool with each other; The generation process of the energy deployment request further includes: If the difference between the real-time operation state data and the corresponding confidence value curve at any time node is less than or equal to the operation deviation threshold, no operation is performed, otherwise, according to the confidence degree of the real-time operation state data in the corresponding numerical section between the time nodes, the real-time operation state data segments between the corresponding time nodes are reduced by multiplying the difference, the confidence degree and the operation deviation correction parameter; The predicted energy demand at each time node in the next operation monitoring period is generated according to the real-time operation state data, and the predicted energy demand between each time node is mapped on the estimated energy demand curve to generate an energy demand interval between each time node; According to the energy demand interval between each time node and the industrial equipment number, an energy deployment request is generated and sent to the power monitoring end.

2. A load adaptive charge management system for an industrial charger as claimed in claim 1, wherein, The collection process of the device state data includes: The type information and production demand of each industrial equipment are obtained, and different operation monitoring periods are set for each industrial equipment according to the type information and production demand, a plurality of sensors are installed on each industrial equipment, and a number is set, and each time the operation monitoring period of an industrial equipment ends, the sensor collects each item of device state data of the industrial equipment.

3. A load adaptive charge management system for an industrial charger as claimed in claim 2, wherein, The acquisition process of the confidence value curve and the estimated energy demand curve includes: n operation time axes are established, a plurality of sub-time sections are divided on the operation time axes according to the time length of the operation monitoring period of each industrial equipment, n is a natural number greater than 0; A two-dimensional coordinate system is established, the device state data corresponding to the same industrial equipment and the same data type and in the sub-time section are mapped on the same two-dimensional coordinate system, and a plurality of time nodes are set in the sub-time section; A plurality of numerical intervals are set, the occurrence times of the device state data segments between each time node in each pair of numerical intervals are counted, and the confidence degrees of the numerical intervals of each time node are set according to the occurrence times. According to the confidence of each numerical interval between each time node, the confidence value between each time node is obtained, the confidence values between each time node are sequentially spliced according to the time node sequence, the confidence value curves of each sub-time section are obtained, and then the confidence value curves corresponding to the voltage and current are used to obtain the estimated energy demand curve of the corresponding sub-time section, and the estimated energy demand curve and the confidence value curves are marked on the operation time axis.

4. A load adaptive charge management system for an industrial charger as claimed in claim 3, wherein, The generation process of the energy deployment request includes: When each operation supervision period ends, the corresponding confidence value curve and the estimated energy demand curve are called on the operation time axis according to the time sequence of the real-time device state data; An operation deviation threshold and a parameter deviation threshold are set, and a plurality of operation deviation correction parameters are set. The device state data is divided into operation state data and parameter state data according to the category. If it is judged that the difference between the real-time parameter state data and the corresponding confidence value curve at any time node is less than or equal to the parameter deviation threshold, the real-time parameter state data segment at the corresponding time node is retained; If it is judged that the difference between the real-time parameter state data and the corresponding confidence value curve at any time node is greater than the parameter deviation threshold, the multiplication result of the difference and the confidence is added to the corresponding real-time parameter state data segment according to the confidence of the numerical interval between the corresponding time nodes.

5. A load adaptive charge management system for an industrial charger as claimed in claim 3, wherein, The setting process of the load evaluation pool and the energy deployment decision pool includes: The maximum load power is obtained, and the real-time energy storage amount and the real-time load power of each industrial charger are obtained by the power supervision end when each operation supervision period starts. The idle load power of the corresponding industrial charger is obtained according to the real-time load power and the maximum load power; A load evaluation pool is set for each industrial charger, and the real-time energy storage amount, the idle load power and the number of the industrial charger are marked in the load evaluation pool. An energy deployment decision pool is set for each industrial device, and the rated operation power, the number and the energy deployment request of the industrial device are marked in the energy deployment decision pool.

6. A load adaptive charge management system for an industrial charger as claimed in claim 5, wherein, The mutual matching process of the load evaluation pool and the energy deployment decision pool includes: When each operation detection period starts, the total energy demand is generated according to the energy demand interval in the energy deployment request of each energy deployment decision pool. The demand priority score is set according to the size of the total energy demand. The maximum energy supply amount of the next operation supervision period is obtained according to the idle load power and the real-time energy storage amount of the load evaluation pool, and the supply priority score is set according to the size of the maximum energy supply amount; According to the size order of the demand priority score and the supply priority score, each energy deployment decision pool and load evaluation pool are matched with each other. If the maximum energy supply amount of the load evaluation pool is greater than or equal to the total energy demand of the energy deployment decision pool, the energy deployment decision of the load evaluation pool is generated according to the total energy demand of the energy deployment decision pool. Otherwise, subtract the maximum energy supply amount of the load evaluation pool from the total energy demand of the energy allocation decision pool, and update the priority score of the corresponding energy allocation decision pool according to the difference.

7. A load adaptive charge management system for an industrial charger as claimed in claim 6, wherein, When the maximum energy supply amount of all load evaluation pools is allocated, or all energy allocation requests are completed, all energy allocation decisions are sent to the corresponding industrial chargers and executed.

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