Load adaptive charging management system of industrial charger

Abstract

The invention discloses a load adaptive charging management system of an industrial charger, relates to the technical field of industrial energy management, and improves the operation stability of industrial equipment. According to multiple equipment state data of each industrial equipment, multiple confidence value curves and estimated energy demand curves of each industrial equipment in an operation supervision period are analyzed, and according to the estimated energy demand curves of each industrial equipment and the confidence value curves, an energy allocation request is generated and sent to a power supply supervision end. A load evaluation pool and an energy allocation decision pool are set, an energy allocation request of industrial equipment is input to the energy allocation decision pool, and then the load evaluation pools and the energy allocation decision pools are matched with each other, and energy allocation decisions are set for the industrial chargers and executed.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial energy management, and in particular to a load adaptation charging management system for an industrial charger. Background Art

[0002] In the field of industrial production, the stable operation of industrial equipment and the rational 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 status data lacked systematic and cyclical planning, making it difficult to comprehensively and accurately understand the operating status of industrial equipment over different time periods. This hindered in-depth analysis of the operational characteristics of industrial equipment, such as determining multiple confidence value curves and estimating energy demand curves during equipment operation. The inability to accurately estimate energy demand led to irrational energy allocation, resulting in either insufficient energy supply, impacting the normal operation of industrial equipment and reducing production efficiency, or excessive energy supply, resulting in energy waste and increased production costs.

[0004] Furthermore, traditional power management methods fail to fully consider real-time energy storage and load power information, making it impossible to establish scientifically sound load assessment pools and energy allocation decision pools for industrial chargers and industrial equipment, respectively. This makes it difficult to effectively match and make decisions when faced with energy allocation requests from industrial equipment, resulting in inefficient energy allocation and failure to achieve optimal energy distribution. Therefore, a load-adaptive charging management system for industrial chargers is proposed. Summary of the Invention

[0005] In order to solve the above technical problems, the object of the present invention is to provide a load adaptation charging management system for an industrial charger.

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

[0007] A load adaptation charging management system for an industrial charger, comprising a load supervision terminal and a power supervision terminal;

[0008] The load monitoring terminal is used to set an operation monitoring cycle, thereby collecting multiple pieces of equipment status data of each industrial equipment under several operation monitoring cycles, and analyzing multiple confidence value curves of each industrial equipment under the operation monitoring cycle and an estimated energy demand curve based on the multiple pieces of equipment status data of each industrial equipment;

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

[0010] The power supply monitoring end is used to set up a load assessment pool for each industrial charger based on the real-time energy storage capacity and load power information, and set up an energy allocation decision pool for each industrial equipment, input the energy allocation request of the industrial equipment into the energy allocation decision pool, and then set and execute the energy allocation decision for each industrial charger by matching the various load assessment pools and energy allocation decision pools with each other.

[0011] Furthermore, the process of collecting the device status data includes:

[0012] Obtain the type information and production requirements of each industrial equipment, and then set different operation supervision cycles for each industrial equipment according to the type information and production requirements;

[0013] Various sensors are installed on each industrial equipment and numbered. Whenever an operation monitoring cycle of an industrial equipment ends, the sensors collect various equipment status data of the industrial equipment.

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

[0015] Establish n operating time axes, and divide the operating time axes into several sub-time segments according to the length of the operating supervision cycle of each industrial equipment, where n is a natural number greater than 0;

[0016] Establish a two-dimensional coordinate system, map the data types and equipment status data corresponding to the same industrial equipment in the sub-time segment to the same two-dimensional coordinate system, and set several time nodes in the sub-time segment;

[0017] Set multiple value intervals, count the number of occurrences of device status data segments between each time node in each pair of value intervals, and then set the confidence level for the value interval of each time node based on the number of occurrences;

[0018] The confidence value is obtained according to the confidence of each numerical interval between each time node. The confidence calculation formula is:

[0019]

[0020] where β i，j Represents the confidence of the jth numerical interval between the i-th pair of time nodes, N j Indicates the number of occurrences of the device status data segment in the jth numerical interval, Num irepresents the total time occurrence count of the device status data segments between the i-th pair of time nodes, p represents the time occurrence count, which represents the time distance between the segments in the device status data collected by the running data collection module starting 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 spliced ​​in sequence according to the time node order to obtain the confidence value curves of each sub-time segment, and then the confidence value curves of the corresponding voltage and current are used to obtain the estimated energy demand curve of the corresponding sub-time segment, and the estimated energy demand curve and the confidence value curves are marked on the operating time axis.

[0022] Furthermore, the process of generating the energy allocation request includes:

[0023] At the end of each operation supervision cycle, the corresponding confidence value curve and estimated energy demand curve are retrieved on the operation timeline according to the corresponding time sequence of the real-time equipment status data;

[0024] Set the operation deviation threshold and parameter deviation threshold, and set various operation deviation correction parameters;

[0025] The device status data is divided into operating status data and parameter status data according to the category. If the difference between the real-time parameter status 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 status data segment at the corresponding time node is retained;

[0026] If it is determined that the difference between the real-time parameter status 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 level is superimposed on the corresponding real-time parameter status data segment based on the confidence level of the numerical segment in which the real-time parameter status data is located between the corresponding time nodes.

[0027] Furthermore, the process of generating the energy allocation request further includes:

[0028] If the difference between the real-time running status 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, based on the confidence of the numerical segment of the real-time running status data between the corresponding time nodes, the real-time running status data segments between the corresponding time nodes are reduced by multiplying the difference, confidence and running deviation correction parameter.

[0029] Generate the predicted energy demand at each time point in the next operation supervision cycle based on various real-time operation status data, and map the predicted energy demand between each time point onto the estimated energy demand curve, thereby generating the energy demand interval between each time point;

[0030] An energy allocation request is generated based on the energy demand interval between each time node and the industrial equipment number and sent to the power supply supervision end.

[0031] Furthermore, the process of setting up the load assessment pool and energy allocation decision pool includes:

[0032] Obtain the setting number of each industrial charger and the maximum load power. Whenever an operation supervision cycle begins, the power supply supervision end obtains the real-time energy storage capacity and real-time load power of each industrial charger, and obtains the idle load power of the corresponding industrial charger based on the real-time load power and the maximum load power.

[0033] Set up a load evaluation pool for each industrial charger, and mark the real-time energy storage capacity, idle load power and number of the industrial charger in the load evaluation pool;

[0034] An energy allocation decision pool is set up for each industrial equipment, and the rated operating power, number and energy allocation request of the industrial equipment are marked in the energy allocation decision pool.

[0035] Furthermore, the matching process between the load assessment pool and the energy allocation decision pool includes:

[0036] At the beginning of each operation detection cycle, the total energy demand is generated according to the energy demand interval in the energy allocation request of each energy allocation decision pool, and the demand priority score is set according to the total energy demand. The maximum energy supply of the next operation supervision cycle is obtained according to the idle load power and real-time energy storage of the load assessment pool, and the supply priority score is set according to the maximum energy supply.

[0037] Based on the order of demand priority scores and supply priority scores, each energy allocation decision pool and load assessment pool are matched with each other. If the maximum energy supply of the load assessment 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 assessment pool based on the total energy demand of the energy allocation decision pool;

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

[0039] Furthermore, when the maximum energy supply of all load assessment pools is allocated, or all energy allocation requests are completed, all energy allocation decisions are sent to the corresponding industrial chargers and executed.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] Based on real-time energy storage and load power information, the present invention sets up a load assessment pool for each industrial charger and an energy allocation decision pool for each industrial device. Energy allocation requests from industrial devices are input into the energy allocation decision pool, and by matching the load assessment pools with the energy allocation decision pools, energy allocation decisions can be made for each industrial charger.

[0042] At the same time, the multi-factor matching method fully considers the actual situation of the power supply and the energy demand of industrial equipment, realizes the optimal distribution of energy, improves energy utilization efficiency, reduces energy waste, and ensures the stable operation of industrial equipment and improves production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

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

[0045] like Figure 1 As shown, a load adaptation charging management system for an industrial charger includes a load supervision terminal and a power supervision terminal;

[0046] The load monitoring terminal is used to set an operation monitoring cycle, thereby collecting multiple pieces of equipment status data of each industrial equipment under several operation monitoring cycles, and analyzing multiple confidence value curves of each industrial equipment under the operation monitoring cycle and an estimated energy demand curve based on the multiple pieces of equipment status data of each industrial equipment;

[0047] Then, based on the estimated energy demand curve of each industrial equipment and the confidence value curve, an energy allocation request is generated and sent to the power supply supervision end;

[0048] The power supply monitoring end is used to set up a load assessment pool for each industrial charger based on the real-time energy storage capacity and load power information, and set up an energy allocation decision pool for each industrial equipment, input the energy allocation request of the industrial equipment into the energy allocation decision pool, and then set and execute the energy allocation decision for each industrial charger by matching the various load assessment pools and energy allocation decision pools with each other.

[0049] Further, the working principle of the present invention is described below by way of examples:

[0050] The load monitoring terminal 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 device in the application scenario, thereby obtaining the type information and production requirements of each industrial device, and then setting different operation supervision cycles for each industrial device according to the type information and production requirements;

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

[0053] Install multiple sensors on each industrial equipment, such as temperature sensors, voltage / current sensors, etc., and connect each sensor to the operation data acquisition module for communication. At the same time, the operation data acquisition module sets the number a1, a2, ..., a for each industrial equipment. n , n is a natural number greater than 0 and represents the total number of industrial equipment in the application scenario;

[0054] Whenever the operation supervision cycle of an industrial equipment ends, the sensors on the corresponding industrial equipment will upload the collected equipment status data to the operation data acquisition module.

[0055] Furthermore, the operation data acquisition module marks the industrial equipment number of each equipment status data and synchronizes all equipment status data with the operation analysis module;

[0056] The operation analysis module is used to analyze multiple confidence value curves of each industrial equipment under the operation supervision cycle and estimate the energy demand curve based on multiple equipment status data of each industrial equipment. The specific process includes:

[0057] Set four operating quarters with a year as the time unit, establish n operating time axes, and divide the operating time axes into four time segments. Then, map each device status data in chronological order above the corresponding time segment of the operating time axis according to the operating quarter corresponding to the collection time of the device status data;

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

[0059] Establish a two-dimensional coordinate system, map the data types and equipment status data corresponding to the same industrial equipment in the sub-time segment to the same two-dimensional coordinate system, and set several time nodes in the sub-time segment;

[0060] Set multiple numerical intervals, count the number of occurrences of device status data segments between each time node in each numerical interval, and then set a confidence level for the numerical interval of each time node based on the number of occurrences. The confidence level is calculated as follows:

[0061]

[0062] where β i，j Represents the confidence of the jth numerical interval between the i-th pair of time nodes, N j Indicates the number of occurrences of the device status data segment in the jth numerical interval, Num i represents the total time occurrence count of the device status data segments between the i-th pair of time nodes, p represents the time occurrence count, which represents the time distance between the segments in the device status data collected by the running data collection module starting from the current time node, 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 α between each time node is obtained, and the confidence value α=q1*β i，1 +……+q j *β i，j ,q j represents the middle value of the jth numerical interval;

[0064] The confidence values ​​α between each time node are sequentially spliced ​​in the order of the time nodes to obtain the confidence value curves of each sub-time segment, and then the estimated energy demand curve of the corresponding sub-time segment is obtained from the corresponding voltage and current confidence value curves. The estimated energy demand curve and the confidence value curves are marked on the operation time axis;

[0065] It should be noted that, whenever an operation supervision cycle ends, the operation analysis module updates the estimated energy demand curve and various confidence value curves on the operation timeline, and synchronizes the updated operation timeline with the energy demand module.

[0066] Furthermore, the energy demand module is used to generate an energy allocation request based on the estimated energy demand curve of each industrial device and the confidence value curve and send it to the power supply monitoring end. The specific process includes:

[0067] At the end of each operation supervision cycle, the energy demand module retrieves the corresponding confidence value curve and estimated energy demand curve on the operation time axis according to the time sequence of the real-time equipment status data;

[0068] Set the operation deviation threshold and parameter deviation threshold, and set various operation deviation correction parameters;

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

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

[0071] If it is determined that the difference between the real-time parameter status data and the corresponding confidence value curve at any time node is greater than the parameter deviation threshold, then the multiplication result of the difference and the confidence level is superimposed on the corresponding real-time parameter status data segment according to the confidence level of the value segment between the corresponding time nodes;

[0072] If the difference between the real-time running status 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, based on the confidence of the numerical segment of the real-time running status data between the corresponding time nodes, the real-time running status data segments between the corresponding time nodes are reduced by multiplying the difference, confidence and running deviation correction parameter.

[0073] Generate the predicted energy demand at each time point in the next operation supervision cycle based on various real-time operation status data, and map the predicted energy demand between each time point onto the estimated energy demand curve, thereby generating the energy demand interval between each time point;

[0074] An energy allocation request is generated based on the energy demand interval between each time node and the industrial equipment number and sent to the power supply supervision end.

[0075] Furthermore, the power supply monitoring terminal obtains the setting number of each industrial charger and obtains the maximum load power, where the numbers are b1, b2, ..., b e , e is a natural number greater than 0;

[0076] And every time an operation supervision cycle begins, the power supervision end obtains the real-time energy storage capacity and real-time load power of each industrial charger, and obtains the idle load power of the corresponding industrial charger based on the real-time load power and maximum load power;

[0077] Set up a load evaluation pool for each industrial charger, and mark the real-time energy storage capacity, idle load power and number of the industrial charger in the load evaluation pool;

[0078] An energy allocation decision pool is set up for each industrial equipment, and the rated operating power, serial number and energy allocation request of the industrial equipment are marked in the energy allocation decision pool;

[0079] At the beginning of each operation detection cycle, the total energy demand is generated according to the energy demand interval in the energy allocation request of each energy allocation decision pool, and the demand priority score is set according to the total energy demand. The maximum energy supply of the next operation supervision cycle is obtained according to the idle load power and real-time energy storage of the load assessment pool, and the supply priority score is set according to the maximum energy supply.

[0080] Based on the order of demand priority scores and supply priority scores, each energy allocation decision pool and load assessment pool are matched with each other. If the maximum energy supply of the load assessment 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 assessment pool based on the total energy demand of the energy allocation decision pool;

[0081] Otherwise, the total energy demand of the energy allocation decision pool is subtracted from the maximum energy supply of the load assessment pool, and the priority score of the corresponding energy allocation decision pool is updated according to the difference. 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 of all load assessment pools is allocated or all energy allocation requests are completed, and then all energy allocation decisions are sent to the corresponding industrial chargers for execution.

[0083] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any indirect modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A load adaptation charging management system for an industrial charger, characterized in that: Including load supervision end and power supervision end; The load monitoring terminal is used to set an operation monitoring cycle, thereby collecting multiple pieces of equipment status data of each industrial equipment under several operation monitoring cycles, and analyzing multiple confidence value curves of each industrial equipment under the operation monitoring cycle and an estimated energy demand curve based on the multiple pieces of equipment status data of each industrial equipment; Then, based on the estimated energy demand curve of each industrial equipment and the confidence value curve, an energy allocation request is generated and sent to the power supply supervision end; The power supply monitoring end is used to set up a load assessment pool for each industrial charger based on the real-time energy storage capacity and load power information, and to set up an energy allocation decision pool for each industrial equipment, input the energy allocation request of the industrial equipment into the energy allocation decision pool, and then set and execute the energy allocation decision for each industrial charger by matching the various load assessment pools and energy allocation decision pools with each other.

2. The load adaptation charging management system for an industrial charger according to claim 1, characterized in that: The process of collecting the device status data includes: Obtain the type information and production requirements of each industrial equipment, and then set different operation supervision cycles for each industrial equipment according to the type information and production requirements. Install multiple sensors on each industrial equipment and set numbers. Whenever the operation supervision cycle of an industrial equipment ends, the sensor collects various equipment status data of the industrial equipment.

3. The load adaptation charging management system for an industrial charger according to claim 2, characterized in that: The process of obtaining the confidence value curve and the estimated energy demand curve includes: Establish n operating time axes, and divide the operating time axes into several sub-time segments according to the length of the operating supervision cycle of each industrial equipment, where n is a natural number greater than 0; Establish a two-dimensional coordinate system, map the data types and equipment status data corresponding to the same industrial equipment in the sub-time segment to the same two-dimensional coordinate system, and set several time nodes in the sub-time segment; Set multiple value intervals, count the number of occurrences of device status data segments between each time node in each pair of value intervals, and then set the confidence level for the value interval of each time node based on the number of occurrences; According to the confidence of each numerical interval between each time node, the confidence value between each time node is obtained, and the confidence values ​​between each time node are sequentially spliced ​​in the order of the time nodes to obtain the confidence value curves of each sub-time segment. Then, the confidence value curves of the corresponding voltage and current are used to obtain the estimated energy demand curve of the corresponding sub-time segment, and the estimated energy demand curve and the confidence value curves are marked on the operating time axis.

4. The load adaptation charging management system for an industrial charger according to claim 3, characterized in that: The process of generating the energy allocation request includes: At the end of each operation supervision cycle, the corresponding confidence value curve and estimated energy demand curve are retrieved on the operation timeline according to the corresponding time sequence of the real-time equipment status data; Set the operation deviation threshold and parameter deviation threshold, as well as set multiple operation deviation correction parameters, and classify the equipment status data into operation status data and parameter status data according to categories. If the difference between the real-time parameter status data and the corresponding confidence value curve at any time node is less than or equal to the parameter deviation threshold, then retain the real-time parameter status data segment at the corresponding time node; If it is determined that the difference between the real-time parameter status 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 level is superimposed on the corresponding real-time parameter status data segment based on the confidence level of the numerical segment in which the real-time parameter status data is located between the corresponding time nodes.

5. The load adaptation charging management system for an industrial charger according to claim 3, characterized in that: The process of generating the energy allocation request further includes: If the difference between the real-time running status 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, based on the confidence of the numerical segment of the real-time running status data between the corresponding time nodes, the real-time running status data segments between the corresponding time nodes are reduced by multiplying the difference, confidence and running deviation correction parameter. Generate the predicted energy demand at each time point in the next operation supervision cycle based on various real-time operation status data, and map the predicted energy demand between each time point onto the estimated energy demand curve, thereby generating the energy demand interval between each time point; An energy allocation request is generated based on the energy demand interval between each time node and the industrial equipment number and sent to the power supply supervision end.

6. The load adaptation charging management system for an industrial charger according to claim 5, characterized in that: The process of setting up the load assessment pool and energy allocation decision pool includes: Obtain the setting number of each industrial charger and the maximum load power. Whenever an operation supervision cycle begins, the power supply supervision end obtains the real-time energy storage capacity and real-time load power of each industrial charger, and obtains the idle load power of the corresponding industrial charger based on the real-time load power and the maximum load power. A load assessment pool is set up for each industrial charger, and the real-time energy storage capacity, idle load power and number of the industrial charger are marked in the load assessment pool. An energy allocation decision pool is set up for each industrial equipment, and the rated operating power, number and energy allocation request of the industrial equipment are marked in the energy allocation decision pool.

7. The load adaptation charging management system for an industrial charger according to claim 6, characterized in that: The matching process between the load assessment pool and the energy allocation decision pool includes: At the beginning of each operation detection cycle, the total energy demand is generated according to the energy demand interval in the energy allocation request of each energy allocation decision pool, and the demand priority score is set according to the total energy demand. The maximum energy supply of the next operation supervision cycle is obtained according to the idle load power and real-time energy storage of the load assessment pool, and the supply priority score is set according to the maximum energy supply. Based on the order of demand priority scores and supply priority scores, each energy allocation decision pool and load assessment pool are matched with each other. If the maximum energy supply of the load assessment 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 assessment pool based on the total energy demand of the energy allocation decision pool; Otherwise, the total energy demand of the energy allocation decision pool is subtracted from the maximum energy supply of the load assessment pool, and the priority score of the corresponding energy allocation decision pool is updated according to the difference.

8. The load adaptation charging management system for an industrial charger according to claim 7, characterized in that: When the maximum energy supply of all load assessment pools has been allocated, or all energy allocation requests have been retrieved, all energy allocation decisions will be sent to the corresponding industrial chargers and executed.

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