Collaborative control method of intelligent robot and energy management system

Abstract

The present invention discloses a collaborative control method for an intelligent robot and an energy management system, and relates to the technical field of intelligent control. The main scheme is: collecting multiple types of data of the robot; calculating operating status indicators, energy consumption historical data indicators and environmental change trend indicators to obtain comprehensive energy evaluation indicators; calculating robot criticality evaluation indicators and energy tension judgment indicators to obtain operation intervention indicators and compare them with thresholds to determine whether to perform operation intervention; according to calculated adjustment parameters, a recovery indicator is obtained, and compared with the threshold to determine whether the initial state is restored; the power fluctuation anomaly coefficient and the temperature anomaly coefficient are calculated to obtain a comprehensive energy consumption anomaly index, and compared with the threshold to determine whether the energy consumption is abnormal; according to the energy distribution adjustment basic coefficient, the energy distribution adjustment ratio is obtained, and compared with the threshold to determine the energy distribution plan; this method can optimize energy distribution and operation intervention decisions, and improve energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a collaborative control method of an intelligent robot and an energy management system. Background Art

[0002] With the continuous development of science and technology and the growing demand for energy, energy management has become a focus of attention in various fields. In scenarios such as industrial production, commercial operations, and family life, efficient use and reasonable allocation of energy are crucial. However, there are currently many problems in energy management, such as serious energy waste, difficulty in accurately controlling robot energy consumption, and unreasonable energy distribution. At the same time, with the widespread application of intelligent robots, the complexity and diversity of robot operating states have also brought huge challenges to energy management. How to achieve real-time monitoring and precise control of many robots has become a problem that needs to be solved urgently.

[0003] In the prior art, independent energy management systems are usually used to monitor and allocate energy. These systems mainly collect energy consumption data of robots through sensors, and then allocate and regulate energy based on preset algorithms and strategies. For example, some energy management systems use power sensors to monitor the power consumption of robots and allocate different energy quotas to them according to the type and importance of the robots.

[0004] However, the existing technology still has some obvious shortcomings. First, the existing energy management system has a relatively single perception of the operating status of the equipment, mainly focusing on the collection of energy consumption data, while insufficient consideration of the actual operating conditions of the equipment, environmental factors, etc. For example, it is impossible to accurately judge whether the equipment is in the best operating state, or the energy allocation plan cannot be adjusted in time when the environment changes. Secondly, the existing technology lacks flexibility and real-time performance in energy allocation. When new equipment is connected or equipment fails, it is often unable to respond quickly and adjust energy allocation, resulting in reduced system efficiency or instability. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the shortcomings of the prior art, the present invention provides a collaborative control method for an intelligent robot and an energy management system. By collecting robot data, various indicators are calculated based on the surrounding environment information data, energy consumption information data and robot criticality data, and operation intervention indicators, adjustment parameters, recovery indicators, comprehensive energy consumption anomaly index, energy allocation adjustment basic coefficient and ratio are obtained. The results are compared with the corresponding thresholds to judge the intervention, recovery, anomaly and energy allocation plan, thus solving the problems of unreasonable energy allocation, incomplete monitoring and lack of intelligence.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a collaborative control method of an intelligent robot and an energy management system, comprising:

[0009] Real-time collection of surrounding environment information data, energy consumption information data and robot criticality data of different robots;

[0010] Based on the energy consumption information data, the operating status indicators are calculated separately and energy consumption historical data indicators ; Calculate environmental change trend indicators based on surrounding environmental information data ; According to the operating status indicators , Energy consumption historical data indicators and environmental change trend indicators , calculate the comprehensive energy assessment index ;

[0011] Based on robot criticality data and comprehensive energy evaluation indicators , calculate the robot criticality evaluation index ; Calculate energy shortage judgment indicators based on energy consumption information data ; Based on comprehensive energy assessment indicators , Robot criticality evaluation indicators and energy stress judgment indicators , calculate the operational intervention index ; Preset operation intervention indicator threshold , the operational intervention indicator and operational intervention indicator thresholds Make a comparison to determine whether to perform operational intervention on the robot;

[0012] Judging indicators based on energy stress and energy consumption information data, calculate adjustment parameters ; According to the adjustment parameters and comprehensive energy assessment indicators , get the recovery index ; Preset recovery indicator threshold , will restore the index and recovery indicator thresholds Compare and determine whether the robot has returned to its initial operating state;

[0013] Based on the energy consumption information data, calculate the power fluctuation abnormality coefficient and temperature anomaly coefficient ;According to the power fluctuation abnormal coefficient and temperature anomaly coefficient , calculate the comprehensive energy consumption abnormality index ; Preset energy consumption abnormal threshold , the comprehensive energy consumption abnormality index Energy consumption abnormal threshold Make comparisons to determine whether the robot's energy consumption is abnormal;

[0014] Based on comprehensive energy consumption abnormality index , calculate the energy allocation adjustment base coefficient ; According to the recovery index and energy allocation adjustment base coefficient , calculate the energy allocation adjustment ratio ; Preset energy allocation threshold and adjust energy allocation ratio Compare with the energy allocation threshold to determine the energy allocation plan.

[0015] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system: calculating the operating status index The method is:

[0016] Energy consumption information data including the robot's power and running time ;

[0017] According to power and running time , calculate the operating status index , the formula based on is:

[0018]

[0019] in, is the power of the i-th robot, i is 1, 2, 3...n; n is the number of robots, which is a positive integer; is the running time of the ith robot.

[0020] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system: calculating the energy consumption historical data index The method is:

[0021] Energy consumption information data also includes historical energy consumption , Current energy consumption and historical energy consumption duration ;

[0022] Based on historical energy consumption , calculate the total historical energy consumption , the formula based on is:

[0023]

[0024] in, is the historical energy consumption of the ith robot;

[0025] Based on current energy consumption , historical total energy consumption and historical energy consumption duration , calculate energy consumption historical data indicators , the calculation formula is:

[0026]

[0027] in, is the historical energy consumption duration of the ith robot.

[0028] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system: calculating the environmental change trend index The method is:

[0029] The surrounding environment information data includes the robot's surrounding temperature , ambient humidity and ambient light intensity ;

[0030] Calculate environmental change trend indicators based on surrounding environmental information data , the formula based on is:

[0031]

[0032] in, is the ambient temperature of the ith robot, The ambient temperature The weight coefficient is between 0.2 and 0.4; is the ambient humidity of the ith robot, The surrounding humidity The weight coefficient is between 0.3 and 0.5; is the ambient light intensity of the ith robot, is the ambient light intensity The weight coefficient is between 0.1 and 0.5; + + =1.

[0033] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system: calculating the comprehensive energy evaluation index The method is:

[0034] According to the operating status indicators , Energy consumption historical data indicators and environmental change trend indicators , calculate the comprehensive energy assessment index , the formula based on is:

[0035]

[0036] in, is the operating status index of the ith robot, Operation status indicator The weight coefficient is between 0.1 and 0.3; is the energy consumption historical data indicator of the ith robot, Energy consumption historical data indicator The weight coefficient is 0.3~0.4; is the environmental change trend indicator of the ith robot, Indicators of environmental change trends The weight coefficient is between 0.3 and 0.5; + + =1.

[0037] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system: calculating the robot criticality evaluation index The method is:

[0038] Robot criticality data including function importance coefficient , Operation correlation index and failure impact factors ;

[0039] Based on robot criticality data and comprehensive energy evaluation indicators , calculate the robot criticality evaluation index , the calculation formula is:

[0040]

[0041] in, is the function importance coefficient of the i-th robot, ranging from 0 to 1; is the operation correlation index of the i-th robot, ranging from 0 to 1; is the fault impact factor of the i-th robot, ranging from 0 to 1; is the comprehensive energy evaluation index of the i-th robot.

[0042] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system, the method for determining whether to perform operation intervention on the robot is:

[0043] Energy consumption information data also includes current energy reserves and energy stress threshold ;

[0044] According to the current energy reserves Energy stress threshold , calculate energy stress judgment index , the formula based on is:

[0045]

[0046] According to the comprehensive energy assessment index , Criticality Evaluation Indicators and energy stress judgment indicators , calculate the operational intervention index , the calculation formula is:

[0047]

[0048] Preset thresholds for operational intervention indicators ;when When No operational intervention is performed on the robot.

[0049] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system, the method for determining whether the robot has resumed the initial operating state is:

[0050] Energy consumption information data also includes current operating parameter values and total energy storage capacity ;

[0051] Judging indicators based on energy stress and current operating parameter values , calculate the adjustment parameters , the calculation formula is:

[0052]

[0053] in, is the parameter adjustment coefficient of the ith robot, ranging from 0.5 to 1;

[0054] According to the current energy reserves , total energy storage capacity , adjust parameters and comprehensive energy assessment indicators , calculate the recovery index , the calculation formula is:

[0055]

[0056] in, is the energy threshold for restoring the initial operating state of the i-th robot;

[0057] Preset recovery indicator threshold ;when , the robot returns to its initial operating state.

[0058] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system, the method for determining whether the energy consumption is abnormal is:

[0059] Based on power , calculate the average power , the formula based on is:

[0060]

[0061] According to power and power average , calculate the power fluctuation anomaly coefficient , the calculation formula is:

[0062]

[0063] Energy consumption information data also includes the current robot temperature and the robot's normal temperature ;

[0064] According to the current robot temperature and the robot's normal temperature , calculate the temperature anomaly coefficient , the calculation formula is:

[0065]

[0066] According to the power fluctuation anomaly coefficient and temperature anomaly coefficient , calculate the comprehensive energy consumption abnormality index , the formula based on is:

[0067]

[0068] in, is the power fluctuation abnormality coefficient of the ith robot, is the power fluctuation anomaly coefficient The weight coefficient is between 0.5 and 0.7; is the temperature anomaly coefficient of the ith robot, is the temperature anomaly coefficient The weight coefficient is between 0.3 and 0.6; and + =1;

[0069] Preset energy consumption abnormality threshold ,when When the robot's energy consumption is abnormal, it is determined that the robot's energy consumption is abnormal.

[0070] In the preferred embodiment of the collaborative control method of the intelligent robot and the energy management system, the method of allocating energy is as follows:

[0071] Based on comprehensive energy consumption abnormality index , calculate the energy allocation adjustment base coefficient , the formula based on is:

[0072]

[0073] According to the recovery index and energy allocation adjustment base coefficient , calculate the energy allocation adjustment ratio , the calculation formula is:

[0074]

[0075] in, The impact factor of new robot access, ranging from 0 to 1;

[0076] Energy allocation threshold includes energy allocation threshold one and energy allocation threshold 2 ,and ;

[0077] when When using the primary energy allocation scheme;

[0078] when When using the secondary energy allocation scheme;

[0079] when A three-level energy allocation scheme is adopted.

[0080] (III) Beneficial effects

[0081] The present invention provides a collaborative control method for an intelligent robot and an energy management system, which has the following beneficial effects:

[0082] (1) Real-time collection of environmental information data, energy consumption information data, and robot criticality data of different robots can fully understand the robot's working environment, energy usage, and importance in the system; this provides an accurate data basis for subsequent energy management and decision-making, helps to achieve refined energy monitoring, and improves energy utilization efficiency.

[0083] (2) Based on the energy consumption information data, the operating status indicators and energy consumption historical data indicators are calculated, and then combined with the surrounding environment information data to calculate the environmental change trend indicators, and then the comprehensive energy evaluation indicators are obtained; this enables the energy management system to comprehensively consider the robot's current operating status, historical energy consumption performance, and the impact of the surrounding environment on energy consumption, providing a multi-dimensional perspective for accurately evaluating energy usage, helping to discover abnormal energy consumption trends in advance and take timely measures to make adjustments.

[0084] (3) Calculate the energy shortage judgment index based on energy consumption information data, calculate the operation intervention index based on the comprehensive energy evaluation index and the robot criticality evaluation index, and compare it with the preset operation intervention index threshold to determine whether to intervene in the robot operation. This process takes timely intervention measures when energy is tight or the robot's operating status is abnormal, avoiding energy waste and equipment damage, and ensuring the stable operation of the system. Through comprehensive judgment of multiple indicators, the timing and method of intervention can be determined more accurately.

[0085] (4) Calculate adjustment parameters based on energy shortage judgment indicators and energy consumption information data, combine adjustment parameters and comprehensive energy evaluation indicators to obtain recovery indicators, and compare with preset recovery indicator thresholds to determine whether the robot has recovered to its initial operating state. This enables the robot's operating parameters to be dynamically adjusted according to actual conditions when energy supply conditions change, thus achieving reasonable energy allocation.

[0086] (5) Based on the energy consumption information data, the power fluctuation anomaly coefficient and the temperature anomaly coefficient are calculated respectively, and then the comprehensive energy consumption anomaly index is calculated, and compared with the preset energy consumption anomaly threshold to determine whether the robot's energy consumption is abnormal. This helps to timely detect abnormal situations in the robot's energy consumption, such as excessive power fluctuations or abnormal temperature increases, so that corresponding measures can be taken to adjust or repair them, avoid equipment failures and energy waste, and improve equipment reliability and energy utilization efficiency.

[0087] (6) Calculate the energy allocation adjustment basic coefficient based on the comprehensive energy consumption abnormality index, calculate the energy allocation adjustment ratio based on the recovery index and the energy allocation adjustment basic coefficient, and compare it with the preset energy allocation threshold to determine the energy allocation plan. This enables the energy management system to dynamically adjust the energy allocation plan according to the actual operation status and energy consumption status of the robot, so as to achieve reasonable allocation and efficient utilization of energy. At the same time, by comparing with the threshold, it can ensure that the energy allocation plan is within a reasonable range, ensuring the stable operation and sustainable development of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is a schematic diagram of the steps of the collaborative control method of the intelligent robot and the energy management system of the present invention. DETAILED DESCRIPTION

[0089] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0090] See also Figure 1 The present invention provides a collaborative control method of an intelligent robot and an energy management system, comprising the following steps:

[0091] Step 1: Collect the surrounding environment information data, energy consumption information data and robot criticality data of different robots in real time.

[0092] Step 101: Select a visual sensor to timely capture changes in the surrounding environment according to the movement speed of the robot, so as to obtain surrounding environment information data; select an energy consumption monitoring sensor, connect the energy consumption monitoring sensor to the power line of the robot, and collect the robot's energy consumption information data in real time; obtain the robot's importance level in the production process by connecting with the production management system, evaluate the robot's functional importance, and obtain the robot's criticality data.

[0093] By collecting information data about the surrounding environment, the energy management system can better understand the robot's working scenario and adjust the energy allocation strategy according to the actual environment; the real-time collection of energy consumption information data helps to accurately grasp the energy consumption of each robot, promptly identify robots with abnormal energy consumption and make targeted adjustments to achieve efficient utilization and conservation of energy; the collection of robot criticality data can clarify the importance of different robots in the system, give priority to the energy needs of key robots when energy supply is tight, ensure that core businesses are not affected, and overall improve the scientificity and effectiveness of the coordinated control of intelligent robots and energy management systems.

[0094] Step 2: Calculate the operating status indicators based on energy consumption information data and energy consumption historical data indicators ; Calculate environmental change trend indicators based on surrounding environmental information data ; According to the operating status indicators , Energy consumption historical data indicators and environmental change trend indicators , calculate the comprehensive energy assessment index .

[0095] Step 201: Monitor the current of the robot in real time based on the energy consumption monitoring sensor and voltage ;

[0096] According to the current and voltage , calculate the power of the robot , the calculation formula is as follows:

[0097]

[0098] in, is the current of the ith robot; is the voltage of the i-th robot;

[0099] A clock module is set for the robot's control system. The time starts when the robot starts and the time is continuously recorded during operation. When the running time parameters need to be obtained, the clock module is directly read to obtain the running time. .

[0100] Step 202: According to the power and running time , calculate the operating status index , the formula based on is:

[0101]

[0102] in, is the power of the ith robot, i is the robot serial number, which can be 1, 2, 3...n; n is the number of robots, which can be a positive integer; is the running time of the ith robot.

[0103] It should be noted that this formula is based on the power of each robot and running time Perform product summation to calculate the operating status index The principle is that power reflects the rate of energy consumption of the robot per unit time, while running time indicates how long the robot continues to consume energy. Multiplying the two together can give the total energy consumption of each robot during a specific running time. Summing the product of the power and running time of all robots can comprehensively reflect the overall energy consumption status and operating conditions of multiple robots in the entire system. If If the value is high, it means that the robot consumes more energy and runs for a long time, and the energy management strategy needs to be further optimized; if A lower value means that the robot consumes less energy or runs for a shorter time, and the system uses energy more efficiently. This indicator can help the energy management system better understand the operating status of the robot cluster so as to adjust energy allocation and optimize control strategies in a timely manner.

[0104] Step 203: Obtain the historical energy consumption of different robots based on the historical data records monitored by the energy consumption monitoring sensor , Current energy consumption and historical energy consumption duration .

[0105] Step 204: Based on historical energy consumption , calculate the total historical energy consumption , the formula based on is:

[0106]

[0107] in, is the historical energy consumption of the ith robot.

[0108] It should be noted that by recording the historical energy consumption of each robot , these individual consumptions are accumulated. Starting from the first robot, its energy consumption is added to the total, and so on until the nth robot. In this way, as the energy consumption of each robot is continuously accumulated, the total energy consumption of the entire system in a specific time period is finally obtained. This process reflects the total energy consumption of all intelligent robots in the system, providing the energy management system with an indicator for a comprehensive understanding of the system's historical energy usage, so as to facilitate subsequent energy analysis, planning and regulation.

[0109] Step 205: Based on current energy consumption , historical total energy consumption and historical energy consumption duration , calculate energy consumption historical data indicators , the calculation formula is:

[0110]

[0111] in, is the historical energy consumption duration of the ith robot.

[0112] It should be noted that the current energy consumption Reflects the energy usage of the robot at the current moment. Historical energy consumption duration It is used to measure the duration of the robot's energy consumption history. Then, the current energy consumption Duration of historical energy consumption Multiply them together to get a product value that takes into account the current consumption level and historical consumption time. Finally, divide this product value by the total historical energy consumption The purpose of this is to compare the current and historical energy consumption of the ith robot with the historical total energy consumption of the entire system, thereby obtaining a relative energy consumption historical data indicator .

[0113] Step 206: Use the temperature sensor on the intelligent robot to measure the surrounding temperature of the robot in real time. The temperature sensor uses the sensitivity of hygroscopic materials to the water vapor content in the environment to measure the humidity around the robot. ; The light intensity sensor installed on the intelligent robot detects the surrounding light intensity of the robot based on the principle of photoelectric effect. .

[0114] Step 207: Based on the ambient temperature , ambient humidity and ambient light intensity , calculate environmental change trend indicators , the formula based on is:

[0115]

[0116] in, is the ambient temperature of the ith robot, The ambient temperature The weight coefficient is between 0.2 and 0.4; is the ambient humidity of the ith robot, The surrounding humidity The weight coefficient is between 0.3 and 0.5; is the ambient light intensity of the ith robot, is the ambient light intensity The weight coefficient is between 0.1 and 0.5; + + =1.

[0117] It should be noted that this formula is intended to comprehensively evaluate the comprehensive influencing factors of the environment in which the i-th robot is located to determine its effect on the robot's operating status or specific performance indicators. is the ambient temperature of the ith robot. The impact of temperature on the robot is reflected in the performance of electronic components, thermal expansion and contraction of mechanical parts, etc.; the weight coefficient is given , indicating that under normal circumstances, the impact of temperature on the robot is moderate, but it will be adjusted in different scenarios. In terms of humidity factors, is the ambient humidity of the ith robot. Humidity affects the electrical insulation performance of the robot and the corrosion rate of metal parts. The weight coefficient is given , which means that humidity has a more important impact on the robot in some cases. In terms of light intensity, is the ambient light intensity of the i-th robot. The light intensity affects the robot's optical sensor performance and solar cell charging efficiency. The weight coefficient is given , indicating that the influence of light intensity is relatively diverse. + + =1 ensures that the sum of the weights of the three factors is 1, so that the final comprehensive index value is a relative measurement under the joint action of the three factors, which can more comprehensively reflect the comprehensive impact of the robot's environment on its performance or status.

[0118] Step 208: Based on the running status indicator , Energy consumption historical data indicators and environmental change trend indicators , calculate the comprehensive energy assessment index , the formula based on is:

[0119]

[0120] in, is the operating status index of the ith robot, Operation status indicator The weight coefficient is between 0.1 and 0.3; is the energy consumption historical data indicator of the ith robot, Energy consumption historical data indicator The weight coefficient is 0.3~0.4; is the environmental change trend indicator of the ith robot, Indicators of environmental change trends The weight coefficient is between 0.3 and 0.5; + + =1.

[0121] It should be noted that the operating status indicator Reflects the working efficiency and health status of the intelligent robot at a specific point in time; weight coefficient Indicates the importance of operating status to energy efficiency. Energy consumption historical data indicators It is the energy consumption of the robot in the past period of time. By analyzing the past energy usage data, we can predict future energy demand and possible energy saving space. Weight coefficient It reflects the reference value of historical energy consumption data for future energy use. The environmental change trend index reflects the impact of external environmental factors on the robot's energy use, such as weather, temperature, humidity, etc. These factors will change the robot's work efficiency and energy demand; for example, in hot weather, the robot has difficulty in heat dissipation, the work efficiency is reduced, and the energy demand increases. At this time, the environmental change trend index will change accordingly. Weight coefficient It reflects the impact of environmental change trends on energy efficiency. + + =1 means that the relative importance of these three indicators can be adjusted according to actual conditions to better reflect the key factors affecting energy efficiency in specific scenarios. The index comprehensively considers the influence of three aspects: operating status, historical data of energy consumption and environmental change trend. Through this index, the energy efficiency of intelligent robots can be comprehensively evaluated, and then a reasonable energy management and allocation strategy can be formulated.

[0122] This method can comprehensively and accurately reflect the energy utilization efficiency of intelligent robots, and help the energy management system to timely adjust the power supply strategy and task scheduling, so as to achieve optimal utilization of energy, improve overall operating efficiency, reduce energy waste, and extend the service life of the robot.

[0123] Step 3: Based on robot criticality data and comprehensive energy evaluation indicators , calculate the robot criticality evaluation index ; Calculate energy shortage judgment indicators based on energy consumption information data ; Based on comprehensive energy assessment indicators , Robot criticality evaluation indicators and energy stress judgment indicators , calculate the operational intervention index ; Preset operation intervention indicator threshold , the operational intervention indicator and operational intervention indicator thresholds Make a comparison to determine whether to perform operational intervention on the robot;

[0124] Step 301: Obtain the function evaluation value of the robot and strategic importance assessment ; Through various standard tests on the robot, record its performance data under different tasks, such as cleaning area, cleaning time, number of failures, etc., score each indicator, and take the average of each score as the functional evaluation value Reference; Functional evaluation value It is a quantitative measurement of the specific functional performance of the robot. It comprehensively considers various performance indicators of the robot when performing specific tasks, including but not limited to accuracy, efficiency, reliability, flexibility and other aspects of completing tasks. For example, for a cleaning robot, accuracy can be reflected in the degree of cleaning of different stains, whether it can completely remove various types of stains without leaving traces, and score them. Analyze the organization's long-term development strategy and short-term business goals, determine the position and role of the tasks undertaken by the robot, and score the robot accordingly. For example, if the company's strategic focus is to improve production efficiency and product quality, then the strategic importance assessment value of the robot that plays a key role in the production line will be higher, so as to obtain the strategic importance assessment value of the robot. ; Strategic importance assessment value It reflects the importance of robots at the overall strategic level of an organization or system. It considers factors such as the contribution of the tasks undertaken by robots to achieving organizational goals, their position in key business processes, and their potential impact on future development. For example, in a logistics distribution center, the efficient work of robots responsible for sorting and moving goods can ensure that goods are delivered to customers in a timely and accurate manner, improve customer satisfaction, and are crucial to the competitiveness of the enterprise. It is necessary to give robots that sort and move goods a higher strategic importance assessment value, such as 7-9 points on a 10-point scale.

[0125] Step 302: According to the function evaluation value obtained in step 301 and strategic importance assessment , calculate the function importance coefficient , the calculation formula is:

[0126]

[0127] in, Function evaluation value The weight coefficient is between 0.5 and 0.6; Assessing the value of strategic importance The weight coefficient is between 0.5 and 0.7, and + =1.

[0128] Step 303: The robot criticality data includes the robot's connection strength coefficient and synchronous operation coefficient .

[0129] By analyzing the robot's communication module and communication protocol, the robot's ability to interact with other devices is evaluated. This includes scoring aspects such as communication stability, data transmission speed, and the number of connectable devices, and calculating the average score. For example, if a robot uses advanced wireless communication technology, can simultaneously transmit high-speed data with multiple devices, and can maintain stable communication in a complex electromagnetic environment, then its connection strength coefficient will be higher. The average of each reference item is calculated to determine the connection strength coefficient. . Connection strength coefficient Reflects the connection strength and interaction ability between the robot and other devices or systems. Robots with high connection strength can work better with other devices to achieve information sharing and optimal resource allocation. For example, these robots can share their own position and task information in real time through wireless communication systems, and dynamically adjust their own paths and work arrangements according to the positions and tasks of other robots. When a robot encounters an obstacle and needs to change its path, other robots can receive the information in time and make corresponding adjustments to ensure the efficient operation of the entire system.

[0130] Through actual multi-robot collaborative scenario testing, the robot's synchronous operation ability is evaluated. Set up different task scenarios to observe the performance of the robot when working with other robots. For example, in a warehouse handling task, multiple robots carry goods at the same time to observe whether they can maintain good synchronization and avoid collisions and congestion. Based on the test results, the robot is scored as the synchronization coefficient Reference; Synchronous operation coefficient A measure of a robot's ability to synchronize with other robots in a multi-robot system. Robots with high synchronization coefficients can better coordinate with other robots and improve the efficiency and stability of the entire system. For example, these robots can share their position and task information in real time through wireless communication systems, and dynamically adjust their paths and work arrangements based on the positions and tasks of other robots. When a robot encounters an obstacle and needs to change its path, other robots can receive the information in time and make corresponding adjustments to ensure the efficient operation of the entire system.

[0131] Step 304: Calculate the connection strength coefficient obtained in step 303 and synchronous operation coefficient , calculate the running correlation index , the formula based on is:

[0132]

[0133] in, is the connection strength coefficient between the ith robot and the jth robot; is the synchronous operation coefficient between the ith robot and the jth robot.

[0134] Step 305: Historical fault data including fault probability , Failure cost coefficient and mean downtime between failures ;

[0135] By recording the total running time of the robot and the number of failures in a period of time, the failure probability is obtained by dividing the number of failures by the total running time. For example, an industrial robot runs for 240 hours in a month and has three failures. Then the failure probability =3÷240=0.0125, which means that the robot has a 1.25% chance of failure per hour of operation.

[0136] By determining the operating cost of the robot over a certain period of time, the various costs after the robot fails are calculated, including maintenance costs, production losses, delay costs, etc. The sum of these costs is compared with the benchmark cost to obtain the failure cost coefficient For example, if a logistics robot fails, the repair cost is 1,000 yuan, and the failure causes a delay in delivery of goods, resulting in a loss of 5,000 yuan. The total failure cost is 6,000 yuan. If the pre-set benchmark cost is 10,000 yuan, then the failure cost coefficient is =6000÷10000=0.6.

[0137] Every time a robot fails, the time of failure and the time of recovery are recorded. The difference between the two is the downtime of this failure. The downtime of multiple failures is statistically averaged to obtain the average downtime of the failure. For example, if a robot has had downtimes of 2 hours, 3 hours, 2.5 hours, 1.5 hours, and 4 hours in the past five failures, then the average downtime TD is (2+3+2.5+1.5+4)÷5=2.6 hours.

[0138] Step 306: According to the failure probability obtained in step 305 , Failure cost coefficient and mean downtime between failures , calculate the fault impact factor , the calculation formula is:

[0139]

[0140] in, Mean downtime The weight coefficient is between 0 and 1.

[0141] Step 307: According to the function importance coefficient , Operation correlation index , Fault influence factors and comprehensive energy assessment indicators , calculate the robot criticality evaluation index , the calculation formula is:

[0142]

[0143] in, is the function importance coefficient of the i-th robot, ranging from 0 to 1; is the operation correlation index of the i-th robot, ranging from 0 to 1; is the fault impact factor of the i-th robot, ranging from 0 to 1; is the comprehensive energy evaluation index of the i-th robot.

[0144] It should be noted that the function importance coefficient It measures the importance of the robot in its entire functional system. It reflects the role it plays in achieving the robot's goals. The higher the value, the greater the contribution to the robot's functional realization. Operational correlation index It reflects the interdependence between robots. The higher the value, the closer the connection. Describes the degree of impact on the entire system when a robot fails. It reflects the harm of the failure to the normal operation of the robot. The higher the value, the greater the loss caused by the failure to the robot. Comprehensive energy evaluation index It is calculated based on the operating status index, energy consumption historical data index and environmental change trend index, and it reflects the energy efficiency of the robot. The higher the value, the better the robot performs in terms of energy use. In the formula, the function importance coefficient is firstly Operational correlation index Multiply them together to get a partial evaluation result, which indicates the criticality of the component or system in terms of function realization and operation. Next, subtract the function importance coefficient from 1. , and obtain a corrected fault impact factor , and then compare it with the comprehensive energy assessment index Multiply them together to get another partial evaluation result, which represents the criticality of the component or system in terms of failure impact and energy efficiency. Finally, add these two partial evaluation results to get the total robot criticality .

[0145] Step 308: Directly measure the current energy reserve through the energy sensor installed on the robot .

[0146] By testing the system in actual operation, the energy consumption of different devices under various working conditions is monitored to determine the minimum energy requirement. .

[0147] By simulating and analyzing the system, various possible situations are considered, such as energy supply fluctuations, equipment failures, etc., and the critical values ​​of normal operation and failure of each robot are scored. The average value is taken as the safety factor. 's reference.

[0148] Step 309: Count the energy consumption data of the robot or system under the influence of external factors such as different seasons, weather conditions and market demand fluctuations. If in a specific season, the temperature is too high or too low, which increases the heat dissipation or heating demand of the robot, thereby increasing energy consumption, the influence of external factors in that season can be determined based on these data and the data under normal conditions, and then the influence coefficient of external factors can be determined. .

[0149] Based on minimum energy requirements , Safety factor and external factors , calculate the energy stress threshold , the calculation formula is:

[0150]

[0151] in, is the minimum energy requirement of the ith robot; is the safety factor of the ith robot.

[0152] Step 310: According to the current energy reserve obtained in step 308 Energy stress threshold , calculate energy stress judgment index , the formula based on is:

[0153]

[0154] Step 311: Based on the comprehensive energy assessment index , Criticality Evaluation Indicators and energy stress judgment indicators , calculate the operational intervention index , the calculation formula is:

[0155]

[0156] It should be noted that in the formula, the comprehensive energy assessment index Criticality Evaluation Indicators Multiply them together to get a partial evaluation result, which represents the robot's operational intervention needs in terms of energy efficiency and criticality. Then, this partial evaluation result is combined with the energy stress judgment index Multiply them together to get the total intervention demand This design makes it possible for energy efficiency, criticality, and energy stress to jointly determine the degree of operational intervention required by the robot.

[0157] Step 312: Preset operation intervention indicator threshold By analyzing the energy consumption data over the past period of time, including power and energy consumption under different working conditions, the energy consumption range of the equipment during normal operation is determined. When the energy consumption exceeds a certain percentage of this range, operational intervention is required. Based on this, the threshold of the operational intervention indicator is preset .

[0158] Step 313: When When No operational intervention is performed on the robot.

[0159] By comprehensively considering the criticality of robots, we can more accurately evaluate the importance of different robots in the system, pay more attention and resource guarantee to key robots, and ensure the stable operation of the core functions of the system. The use of comprehensive energy evaluation indicators can make energy allocation more reasonable and efficient, reduce energy consumption and improve energy utilization efficiency while ensuring the normal operation of robots. The energy shortage judgment index calculated based on energy consumption information data helps to adjust strategies in time when energy is tight, giving priority to the operation of key tasks and important robots. By presetting and comparing the thresholds of operation intervention indicators, an automated and intelligent decision-making process can be realized, reducing the cost and uncertainty of manual intervention, improving the system's response speed and decision-making accuracy, and thus improving the overall stability, reliability and operation efficiency of the energy management system.

[0160] Step 4: Determine indicators based on energy stress and energy consumption information data, calculate adjustment parameters ; According to the adjustment parameters and comprehensive energy assessment indicators , get the recovery index ; Preset recovery indicator threshold , will restore the index and recovery indicator thresholds Compare and determine whether the robot has returned to its initial operating state;

[0161] Step 401: directly read the scores of the parameter values ​​of each part measured by the sensor on the robot, such as speed parameter, temperature parameter, current parameter, pressure parameter and position parameter, etc., through the interface connected to the sensor, such as analog input port, digital communication bus, etc.; compare the score of each part parameter with the rated parameter value of the normal operating state, the closer to the rated parameter value, the higher the score, and score the parameters, and average the scores of each item to obtain the current operating parameter value For example, if the speed parameter score is 8 points, the temperature parameter score is 7 points, the current parameter score is 7 points, the pressure parameter score is 8 points, and the position parameter score is 8 points, then the average score is (8+7+7+8+8) / 5=7.6 points. This 7.6 points can be used as the current operating parameter value PC.

[0162] Check the battery specification sheet of the robot to find the battery capacity information and get the total energy storage capacity .

[0163] Judging indicators based on energy stress and current operating parameter values , calculate the adjustment parameters , the calculation formula is:

[0164]

[0165] in, is the parameter adjustment coefficient of the ith robot, ranging from 0.5 to 1.

[0166] It should be noted that the current running parameter value , Energy stress judgment indicators And parameter adjustment coefficient Working together to calculate the tuning parameters When energy is tight, the indicators When it increases, it indicates that energy supply is tight. The value of becomes smaller, so that the current operating parameter value is multiplied by a smaller coefficient during the adjustment process, thereby reducing the adjusted parameter value and achieving the purpose of reducing the robot's energy consumption. It provides personalized adjustment ranges for different robots. The coefficients of key robots or robots that are insensitive to energy changes may be close to 1, with a small adjustment range; the coefficients of non-key robots or robots that are sensitive to energy may be small, with a large adjustment range. Overall, this formula can reasonably allocate energy according to the energy supply status and robot characteristics, ensure the execution of key tasks when energy is tight, and appropriately improve the robot's operating performance when energy is loose, thereby improving the overall efficiency of the system.

[0167] Step 402: Record the energy consumed by the robot when it returns to the initial operating state after each failure or energy exhaustion. After a period of accumulation and statistical analysis, find a critical energy value as a reference for the energy threshold for returning to the initial operating state. Based on this, the energy threshold for returning to the initial operating state of the robot is obtained. .

[0168] According to the current energy reserves , total energy storage capacity , adjust parameters , Comprehensive energy assessment indicators and energy thresholds for restoring initial operating status , calculate the recovery index , the calculation formula is:

[0169]

[0170] in, is the energy threshold for the i-th robot to recover to the initial operating state.

[0171] It should be noted that Indicates the difference between the current energy reserve and the energy value required for the robot to restore to the initial operating state. This difference reflects the surplus or shortage of the current actual energy reserve relative to the initial state. If the current energy reserve is greater than the energy value required to restore the initial operating state, the difference is positive, indicating that there is a certain amount of energy left for the recovery operation; otherwise, the difference is negative, indicating that the energy is insufficient and other factors need to be considered to achieve recovery. Plus , taking into account the adjustment of the robot's operating parameters due to the current energy tension, adding it to the numerator further reflects the adjusted energy availability under different energy conditions. It represents the difference between the total energy reserve capacity and the energy value required for the robot to restore its initial operating state. This difference represents the maximum energy margin that the entire system can theoretically provide for recovery operations. The stability and quality of energy supply and the impact of equipment failure on energy are comprehensively considered. The relative energy surplus ratio calculated above is multiplied by the comprehensive energy evaluation index, so that the recovery index not only considers the pure energy quantity factor, but also combines the overall quality and stability of energy supply. If the energy supply is stable and of high quality, A larger value will increase the recovery index accordingly, indicating that the possibility and degree of the robot recovering to normal operation is higher in this case; on the contrary, if the energy supply is unstable or of low quality, A smaller value will reduce the recovery index, which means that the difficulty of recovery increases.

[0172] Step 403: Preset recovery index threshold ; By analyzing the robot's recovery to the initial operating state under different energy conditions and operating scenarios in the past. The corresponding energy reserve, adjustment parameters, and comprehensive energy evaluation indicators in each recovery process are counted to calculate the corresponding recovery index value. By analyzing these historical recovery index values, a typical value range that can represent the successful recovery of the initial operating state is determined, and a suitable value is selected as the preset recovery index threshold. Based on this, the recovery index threshold is preset ;

[0173] when , the robot returns to its initial operating state.

[0174] This method can dynamically adjust the robot operation according to the actual degree of energy tension, reasonably reduce energy consumption when energy is tight, and ensure stable operation of the system; the comprehensive energy evaluation index ensures the comprehensiveness and accuracy of the recovery decision, so that the robot can restore the initial operating state at the right time, improve the overall efficiency and reliability of the system, and also help to achieve efficient management and reasonable allocation of energy.

[0175] Step 5: Calculate the power fluctuation anomaly coefficient based on the energy consumption information data and temperature anomaly coefficient ;According to the power fluctuation abnormal coefficient and temperature anomaly coefficient , calculate the comprehensive energy consumption abnormality index ; Preset energy consumption abnormal threshold , the comprehensive energy consumption abnormality index Energy consumption abnormal threshold Make comparisons to determine whether the robot's energy consumption is abnormal;

[0176] Step 501: Measure and collect the current robot temperature using the temperature sensor and the robot's normal temperature ;

[0177] According to the current robot temperature and the robot's normal temperature , calculate the temperature anomaly coefficient , the calculation formula is:

[0178]

[0179] It should be noted that the current robot temperature is first calculated and the robot's normal temperature The absolute value of the difference, that is , this value reflects the degree of deviation between the current temperature and the normal temperature. Then divide this deviation value by the normal temperature of the robot The result is the temperature anomaly coefficient This coefficient can measure the degree of abnormality of the current temperature relative to the normal temperature. The larger the coefficient, the greater the difference between the current temperature and the normal temperature, and the more serious the temperature abnormality.

[0180] Step 502: According to the power fluctuation abnormal coefficient and temperature anomaly coefficient , calculate the comprehensive energy consumption abnormality index , the formula based on is:

[0181]

[0182] in, is the power fluctuation abnormality coefficient of the ith robot, is the power fluctuation anomaly coefficient The weight coefficient is between 0.5 and 0.7; is the temperature anomaly coefficient of the ith robot, is the temperature anomaly coefficient The weight coefficient is between 0.3 and 0.6; and + =1;

[0183] It should be noted that this formula uses the power fluctuation anomaly coefficient and temperature anomaly coefficient To calculate the comprehensive energy consumption abnormality index .in, and are the weight coefficients of the power fluctuation anomaly coefficient and the temperature anomaly coefficient, and the sum of the two is 1. This means that the anomaly index is the result of comprehensive consideration of power fluctuation and temperature anomaly. If the power fluctuation has a relatively large impact on the system, then The value of will be relatively large. On the contrary, if the influence of temperature anomaly is more prominent, The value of will be relatively large. and After multiplying by the corresponding weight coefficients, we get an index that comprehensively reflects the degree of abnormal operation of the robot. .

[0184] Step 503: Preset abnormal energy consumption threshold By analyzing the robot's operating data over the past period of time, find out those moments that are clearly determined to be energy consumption abnormalities. Count the power fluctuation abnormality coefficients, temperature abnormality coefficients, and abnormality indexes calculated at the time corresponding to these moments. By analyzing these historical data, determine an abnormal index range that can represent energy consumption abnormalities, and select a suitable value as the preset energy consumption abnormality threshold. Based on this, preset the energy consumption abnormality threshold ;

[0185] when When the robot's energy consumption is abnormal, it is determined that the robot's energy consumption is abnormal.

[0186] Step 6: Based on the comprehensive energy consumption abnormality index , calculate the energy allocation adjustment base coefficient ; According to the recovery index and energy allocation adjustment base coefficient , calculate the energy allocation adjustment ratio ; Preset energy allocation threshold and adjust energy allocation ratio Compare with the energy allocation threshold to determine the energy allocation plan.

[0187] Step 601: Based on the comprehensive energy consumption abnormality index , calculate the energy allocation adjustment base coefficient , the formula based on is:

[0188]

[0189] Step 602: Based on the recovery index and energy allocation adjustment base coefficient , calculate the energy allocation adjustment ratio , the calculation formula is:

[0190]

[0191] in, The impact factor of new robot access is between 0 and 1.

[0192] It should be noted that the recovery index It reflects the possibility and degree of the robot returning to normal operation under the current energy conditions. When the value is large, it means that the robot has a greater potential to return to normal state, and the demand for energy allocation adjustment may be relatively high. Energy allocation adjustment basic coefficient Represents a basic ratio for adjusting the energy allocation of the robot without the influence of other factors. This coefficient can be determined based on factors such as the type, importance, and historical energy demand of the robot. The impact of newly added robots on energy distribution is considered. When a new robot is added to the system, it may change the energy demand and distribution of the entire system. A positive value indicates that the addition of new robots increases the need for energy allocation adjustment; if If the value is 0, it means that the access of the new robot has no effect on the energy allocation adjustment of the current robot. , Energy allocation adjustment basic coefficient as well as Multiply the three together to get the energy allocation adjustment ratio .

[0193] Step 603: Preset energy allocation thresholds, which include energy allocation threshold 1 and energy allocation threshold 2 ,and ; By collecting detailed operating data of the system in the past period of time, including the energy allocation adjustment ratio in different scenarios, the operating status of the robot, energy consumption data and the overall performance of the system. Through statistical analysis of a large amount of historical data, some key ratio nodes can be determined, which can be used as reference values ​​for thresholds. If it is found in historical data that when the energy allocation adjustment ratio is less than a certain value A, the system's energy utilization efficiency is high and the robot runs stably; based on this, the energy allocation threshold is preset. When the ratio is between A and B, the system performance begins to fluctuate but is still within an acceptable range; when the ratio is greater than B, the system will experience severe energy shortage or robot failure. Based on this, the energy allocation threshold 2 is preset. .

[0194] Step 604: When When using the first-level energy distribution plan, it distributes energy according to the basic needs of the robots to ensure that each robot can obtain enough energy to maintain normal operation.

[0195] when When the energy allocation is low, a two-level energy allocation scheme is adopted; for robots with critical tasks, the energy allocation ratio can be appropriately increased to ensure that they can successfully complete the task; for robots with non-critical tasks, the energy allocation ratio can be appropriately reduced, but their basic operating needs must be guaranteed.

[0196] when When the energy supply is high, a three-level energy allocation scheme is adopted. An emergency energy allocation strategy is adopted to prioritize the energy supply of mission-critical and important robots, and to impose strict energy restrictions on non-mission-critical robots.

[0197] By calculating the energy allocation adjustment basic coefficient and the energy allocation adjustment ratio, energy allocation can be carried out more scientifically and reasonably according to the current energy status of the system and the actual needs of the robot. When the comprehensive energy consumption is abnormal, the energy allocation strategy can be adjusted in time to avoid energy waste and unreasonable allocation. Combined with the recovery index, energy allocation can be carried out while considering the robot's need to restore normal operating status, thereby improving energy utilization efficiency. Preset energy allocation thresholds and compare them to determine the energy allocation plan, making energy allocation more accurate and targeted, and being able to automatically switch to the appropriate allocation plan under different energy conditions, ensuring the stable operation of the system and the smooth completion of key tasks, and achieving efficient management and optimal configuration of energy.

[0198] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware or in combination with computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0199] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0200] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A collaborative control method of an intelligent robot and an energy management system, characterized in that: include: Real-time collection of surrounding environment information data, energy consumption information data and robot criticality data of different robots; Based on the energy consumption information data, the operating status indicators are calculated separately and energy consumption historical data indicators ; Calculate operating status indicators The method is: Energy consumption information data including the robot's power and running time ; According to power and running time , calculate the running status index , the formula based on is: in, is the power of the i-th robot, i is 1, 2, 3...n; n is the number of robots, which is a positive integer; is the running time of the ith robot; Calculate historical energy consumption indicators The method is: Energy consumption information data also includes historical energy consumption , Current energy consumption and historical energy consumption duration ; Based on historical energy consumption , calculate the total historical energy consumption , the formula based on is: in, is the historical energy consumption of the ith robot; Based on current energy consumption , historical total energy consumption and historical energy consumption duration , calculate energy consumption historical data indicators , the calculation formula is: in, is the historical energy consumption duration of the i-th robot; Calculate environmental change trend indicators based on surrounding environmental information data ; Calculate environmental change trend indicators The method is: The surrounding environment information data includes the robot's surrounding temperature , ambient humidity and ambient light intensity ; Calculate environmental change trend indicators based on surrounding environmental information data , the formula based on is: in, is the ambient temperature of the ith robot, The ambient temperature The weight coefficient is between 0.2 and 0.4; is the ambient humidity of the ith robot, The surrounding humidity The weight coefficient is between 0.3 and 0.5; is the ambient light intensity of the ith robot, is the ambient light intensity The weight coefficient is between 0.1 and 0.5; + + =1; According to the operating status indicators , Energy consumption historical data indicators and environmental change trend indicators , calculate the comprehensive energy assessment index ; Based on robot criticality data and comprehensive energy evaluation indicators , calculate the robot criticality evaluation index ; Calculate energy shortage judgment indicators based on energy consumption information data ; Based on comprehensive energy assessment indicators , Robot criticality evaluation indicators and energy stress judgment indicators , calculate the operational intervention index ; Preset operation intervention indicator threshold , the operational intervention indicator and operational intervention indicator thresholds Make a comparison to determine whether to perform operational intervention on the robot; Judging indicators based on energy stress and energy consumption information data, calculate adjustment parameters ; According to the adjustment parameters and comprehensive energy assessment indicators , get the recovery index ; Preset recovery indicator threshold , will restore the index and recovery indicator thresholds Compare and determine whether the robot has returned to its initial operating state; Based on the energy consumption information data, calculate the power fluctuation abnormality coefficient and temperature anomaly coefficient ;According to the power fluctuation abnormal coefficient and temperature anomaly coefficient , calculate the comprehensive energy consumption abnormality index ; Preset energy consumption abnormal threshold , the comprehensive energy consumption abnormality index Energy consumption abnormal threshold Make comparisons to determine whether the robot's energy consumption is abnormal; Based on comprehensive energy consumption abnormality index , calculate the energy allocation adjustment base coefficient ; According to the recovery index and energy allocation adjustment base coefficient , calculate the energy allocation adjustment ratio ; Preset energy allocation threshold and adjust energy allocation ratio Compare with the energy allocation threshold to determine the energy allocation plan.

2. The collaborative control method of the intelligent robot and the energy management system according to claim 1 is characterized in that: Calculate comprehensive energy assessment indicators The method is: According to the operating status indicators , Energy consumption historical data indicators and environmental change trend indicators , calculate the comprehensive energy assessment index , the formula based on is: in, is the operating status index of the ith robot, Operation status indicator The weight coefficient is between 0.1 and 0.3; is the energy consumption historical data indicator of the ith robot, Energy consumption historical data indicator The weight coefficient is 0.3~0.4; is the environmental change trend indicator of the ith robot, Indicators of environmental change trends The weight coefficient is between 0.3 and 0.5; + + =1.

3. The collaborative control method of the intelligent robot and the energy management system according to claim 2 is characterized in that: Calculate robot criticality evaluation indicators The method is: Robot criticality data including function importance coefficient , Operation correlation index and failure impact factors ; Based on robot criticality data and comprehensive energy evaluation indicators , calculate the robot criticality evaluation index , the calculation formula is: in, is the function importance coefficient of the i-th robot, ranging from 0 to 1; is the operation correlation index of the i-th robot, ranging from 0 to 1; is the failure impact factor of the ith robot, The value range is 0~1; is the comprehensive energy evaluation index of the i-th robot.

4. The collaborative control method of the intelligent robot and the energy management system according to claim 3 is characterized in that: The method for determining whether to intervene in the operation of the robot is: Energy consumption information data also includes current energy reserves and energy stress threshold ; According to the current energy reserves Energy stress threshold , calculate energy stress judgment index , the formula based on is: According to the comprehensive energy assessment index , Criticality Evaluation Indicators and energy stress judgment indicators , calculate the operational intervention index , the calculation formula is: Preset thresholds for operational intervention indicators ;when When No operational intervention is performed on the robot.

5. The collaborative control method of the intelligent robot and the energy management system according to claim 4 is characterized in that: The method to determine whether the robot has resumed its initial operating state is: Energy consumption information data also includes current operating parameter values and total energy storage capacity ; Judging indicators based on energy stress and current operating parameter values , calculate the adjustment parameters , the calculation formula is: in, is the parameter adjustment coefficient of the ith robot, ranging from 0.5 to 1; According to the current energy reserves , total energy storage capacity , adjust parameters and comprehensive energy assessment indicators , calculate the recovery index , the calculation formula is: in, is the energy threshold for restoring the initial operating state of the i-th robot; Preset recovery indicator threshold ;when , the robot returns to its initial operating state.

6. The collaborative control method of the intelligent robot and the energy management system according to claim 5 is characterized in that: The method to determine whether energy consumption is abnormal is: Based on power , calculate the average power , the formula based on is: According to power and power average , calculate the power fluctuation anomaly coefficient , the calculation formula is: Energy consumption information data also includes the current robot temperature and the robot's normal temperature ; According to the current robot temperature and the robot's normal temperature , calculate the temperature anomaly coefficient , the calculation formula is: According to the power fluctuation anomaly coefficient and temperature anomaly coefficient , calculate the comprehensive energy consumption abnormality index , the formula based on is: in, is the power fluctuation abnormality coefficient of the ith robot, is the power fluctuation anomaly coefficient The weight coefficient is between 0.5 and 0.7; is the temperature anomaly coefficient of the ith robot, is the temperature anomaly coefficient The weight coefficient is between 0.3 and 0.6; and + =1; Preset energy consumption abnormality threshold ,when , it is determined that the robot's energy consumption is abnormal.

7. The collaborative control method of the intelligent robot and the energy management system according to claim 6 is characterized in that: The method of allocating energy is as follows: Based on comprehensive energy consumption abnormality index , calculate the energy allocation adjustment base coefficient , the formula based on is: According to the recovery index and energy allocation adjustment base coefficient , calculate the energy allocation adjustment ratio , the calculation formula is: in, The impact factor of new robot access, ranging from 0 to 1; Energy allocation threshold includes energy allocation threshold one and energy allocation threshold 2 ,and ; when When using the primary energy allocation scheme; when When using the secondary energy allocation scheme; when A three-level energy allocation scheme is adopted.

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