IOC-based intelligent operation management method and system

Abstract

The invention discloses an intelligent operation management method and system based on IOC, and relates to the technical field of intelligent operation management, and the method comprises the steps: collecting equipment, people flow and data in a park, and carrying out the preprocessing; designing an intelligent scheduling algorithm based on the preprocessed data, allocating resources, analyzing the current resource allocation state of the park, and generating a new scheduling plan; executing new resource allocation according to the new scheduling plan; generating feedback information according to the execution result; updating a data set of an intelligent scheduling algorithm according to feedback information, optimizing a resource allocation scheme, adjusting resource allocation through actual operation, ensuring that resources are allocated according to an optimal plan, generating feedback information by utilizing an execution result, updating the data set of the intelligent scheduling algorithm according to the feedback information, and continuously optimizing the algorithm. And continuous improvement of a resource allocation scheme is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation management, and in particular to an IOC-based intelligent operation management method and system. Background Art

[0002] With the rapid development of the Internet of Things and big data technologies, intelligent operation management systems are being used more and more widely in industrial production and commercial operations. Traditional resource management and scheduling methods have gradually shown many limitations in the face of increasingly complex park operation needs. It is difficult to respond to emergencies in real time, resulting in low resource utilization and even waste of resources.

[0003] In the existing technology, although advanced data processing technology and intelligent algorithms are introduced to improve park resource management, the dynamic adaptability of intelligent scheduling algorithms still needs to be improved. When the equipment status, personnel flow and environmental conditions in the park change, the scheduling strategy often cannot be adjusted in time, resulting in unreasonable resource allocation, which in turn affects the continuous optimization of the resource allocation plan. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an IOC-based intelligent operation management method and system to solve the problems of unreasonable resource allocation, untimely scheduling and insufficient optimization in existing park operation management.

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

[0007] In a first aspect, an embodiment of the present invention provides an IOC-based intelligent operation management method and system, which includes collecting equipment, personnel flow and data in the park and performing pre-processing;

[0008] Based on the preprocessed data, design intelligent scheduling algorithms to allocate resources, analyze the current resource allocation status of the park and generate new scheduling plans;

[0009] Execute new resource allocation according to the new scheduling plan;

[0010] Generate feedback information based on the execution results;

[0011] Update the data set of the intelligent scheduling algorithm based on the feedback information and optimize the resource allocation plan.

[0012] As a preferred solution of the IOC-based intelligent operation management method and system described in the present invention, the collection of equipment, personnel flow and data in the park and preprocessing include the following steps: collecting the working status of equipment in the park, resource availability, usage frequency and energy consumption data, collecting the time, path and stay area data of personnel flow in and out, collecting temperature, humidity and light intensity data, and cleaning, formatting and deduplicating the collected data.

[0013] As a preferred solution of the IOC intelligent operation management method and system described in the present invention, wherein:

[0014] Based on the preprocessed data, an intelligent scheduling algorithm is designed to allocate resources, analyze the current resource allocation status of the park and generate a new scheduling plan, including the following steps:

[0015] The equipment operation status, human flow status and data are obtained from the preprocessed data to form a state vector and a data set. The architecture of the DQN model is defined. The preprocessed data is selected from the data set to train the DQN model. The trained DQN model is used to calculate the Q corresponding to each action a. total (s, a) value;

[0016] Action a is to adjust the workload of the equipment, Q total (s, a) is the total benefit of taking action a in state s;

[0017] Choose Q from all actions total (s, a) best action a * , the best action a is selected * The resource allocation instruction is converted into a specific control signal, and the corresponding resource allocation is performed according to the generated control signal;

[0018] Design an intelligent scheduling algorithm to allocate resources according to the actual needs of the park and define the objective function of the algorithm to minimize the total cost.

[0019]

[0020] Among them, J(P) is the minimum total cost, U i (P i ) is the i-th task P i The resource utilization under the condition, n is the number of tasks, P is the scheduling plan, α is the parameter for adjusting the steepness of the resource utilization curve, β is the parameter for adjusting the steepness of the cost curve, μ U is the average resource utilization, D(P) is the cost-adjusted sum of all tasks, and i is the task index;

[0021] Collect the status data of current resources and input the availability of resources into the intelligent scheduling algorithm to obtain the resource demand forecast value, which is expressed as:

[0022] R(t)=A(t)×(1+exp(-λ(Z(t)-θ))) -1 +∈(t);

[0023] Among them, R(t) is the predicted value of resource demand at time t, Z(t) is the linear combination of factors affecting resource demand at time t, ∈(t) is the random error term, λ is the adjustment parameter, θ is the baseline value of Z(t), and A(t) represents resource availability.

[0024] As a preferred solution of the IOC intelligent operation management method and system described in the present invention, wherein:

[0025] Set the deviation threshold. When the predicted value R(t)>, the resource allocation is performed through the intelligent scheduling algorithm.

[0026] Adjust and generate a new scheduling plan, the expression is:

[0027]

[0028] Among them, P new is a new scheduling plan, P∈P is an element in the set P, D f (P f ) is the fth resource unit P f The delay value, P f is the allocation of the f-th resource, u is the range of the sum of delays of all resource units, and f is the resource unit index.

[0029] As a preferred solution of the IOC intelligent operation management method and system described in the present invention, wherein:

[0030] According to the new scheduling plan, executing new resource allocation includes the following steps:

[0031] Based on the specific dispatch instructions generated by the selected best action, the dispatch instructions are sent to the operator and the collected data are input into the state vector;

[0032] Use anomaly detection algorithms to analyze the collected data and standardize the data for easy comparison and analysis. When the utilization of the equipment is higher than the historical average, the energy consumed by the equipment is higher than the historical average, and there is a deviation from the expected behavior, take remedial measures, and record the problems encountered during the execution that did not operate as expected, using a structured log format;

[0033] Collect various equipment utilization and energy consumption indicators during the execution process;

[0034] The weighted average formula is used to calculate the comprehensive score of each indicator;

[0035]

[0036] Among them, C s It is composed of multiple indicators. v is the actual value of the v index, ω ν is the importance weight of the vth indicator, ω j is the importance weight of the jth indicator, τ is the total number of indicators for comprehensive score calculation;

[0037] Use the deviation calculation formula to calculate the deviation between the comprehensive score of the indicator and the target comprehensive score. The expression is:

[0038]

[0039] Among them, C s is the comprehensive score, C target Give your target a comprehensive score;

[0040] When the deviation is large, new resource allocation needs to be performed.

[0041] As a preferred solution of the IOC-based intelligent operation management method and system described in the present invention, executing new resource allocation and generating feedback information includes the following steps:

[0042] Executing new resource allocation and generating feedback information includes the following steps:

[0043] Summarize the personnel situation, equipment operation status and environmental condition data during the execution process to form a detailed feedback report;

[0044] The actual workload of the equipment deviates from the expected workload;

[0045] The actual ambient temperature deviates from the expected temperature;

[0046] The actual flow of people deviates from the expected flow of people;

[0047] When the deviation is large, the scheduling plan needs to be adjusted and the scheduling process needs to be re-entered.

[0048] As a preferred solution of the IOC intelligent operation management method and system described in the present invention, wherein: updating the data set of the intelligent scheduling algorithm according to the feedback information and optimizing the resource allocation scheme include the following steps:

[0049] Extract execution results, deviation analysis, and problem records from feedback reports;

[0050] Retrain the smart scheduling model using updated data;

[0051] Introduce new feature variables, such as equipment maintenance history and personnel flow distribution, to improve the prediction accuracy of the model and add them to the state vector;

[0052] Use the optimized model to conduct a new round of simulation experiments in the simulation environment to evaluate the performance of the model on the new data set;

[0053] By comparing the differences between the simulation results and the actual execution results, the performance of the model was evaluated. The model performed better than the intelligent scheduling algorithm and was deployed in the actual environment. If it did not outperform the intelligent scheduling algorithm, it would continue to be adjusted and optimized.

[0054] In a second aspect, the present invention provides an IOC-based intelligent operation management system, including a data collection and preprocessing module, which collects equipment, personnel flow and data in the park and performs preprocessing;

[0055] Intelligent scheduling algorithm module, based on pre-processed data, designs intelligent scheduling algorithms to allocate resources, analyze the current resource allocation status of the park and generate new scheduling plans;

[0056] The resource allocation execution module executes new resource allocation according to the new scheduling plan;

[0057] A feedback information generation module generates feedback information according to the execution result;

[0058] The data set update and optimization module updates the data set of the intelligent scheduling algorithm based on feedback information and optimizes the resource allocation plan.

[0059] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the IOC-based intelligent operation management method and system as described in the first aspect of the present invention is implemented.

[0060] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the IOC-based intelligent operation management method and system as described in the first aspect of the present invention is implemented.

[0061] The beneficial effects of the present invention are as follows: by collecting equipment, personnel flow and data in the park and performing preprocessing, data standardization is achieved, reliable basic data is provided for the intelligent scheduling algorithm, thereby improving the accuracy and reliability of scheduling decisions, the preprocessed data is used to design an intelligent scheduling algorithm, and the resource allocation strategy is dynamically adjusted according to real-time data. By analyzing the current resource allocation status, existing problems are discovered and adjusted in a timely manner, a new scheduling plan is generated, and new resource allocation is executed according to the new scheduling plan. The scheduling plan generated by the intelligent scheduling algorithm is converted into a specific control signal, and resource allocation is adjusted through actual operation to ensure that resources are allocated according to the optimal plan. Feedback information is generated using the execution result, and the data set of the intelligent scheduling algorithm is updated according to the feedback information. The algorithm is continuously optimized to ensure continuous improvement of the resource allocation plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 This is a flow chart of the IOC intelligent operation management method in Example 1.

[0064] Figure 2 This is a flow chart of the IOC intelligent operation management system in Example 1. DETAILED DESCRIPTION

[0065] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0066] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0068] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, and provides an IOC-based intelligent operation management method and system, including the following steps:

[0069] S1, collects equipment, personnel flow and data in the park and performs pre-processing;

[0070] Among them, the working status of equipment in the park, the availability of resources, the frequency of use and energy consumption data, the time, path and stay area of ​​people entering and leaving the park, and the temperature, humidity and light intensity data are collected;

[0071] Furthermore, by collecting data on the working status of equipment, resource availability, usage frequency, and energy consumption, equipment failures and resource shortages can be discovered in a timely manner, thus improving the reliability of equipment operation and the efficiency of resource utilization;

[0072] By collecting data on the time, path, and stop areas of people entering and leaving, we can optimize crowd management, avoid congestion, and improve user experience.

[0073] By collecting temperature, humidity and light intensity data, timely adjust environmental control strategies to improve comfort and reduce energy consumption;

[0074] By cleaning, formatting and deduplicating the data, the quality of the data is ensured and the accuracy and reliability of the subsequent intelligent scheduling algorithm are improved.

[0075] S2, based on the preprocessed data, designs an intelligent scheduling algorithm to allocate resources, analyzes the current resource allocation status of the park and generates a new scheduling plan, including the following steps:

[0076] The equipment operation status, human flow status and data are obtained from the preprocessed data to form a state vector and a data set. The architecture of the DQN model is defined. The preprocessed data is selected from the data set to train the DQN model. The trained DQN model is used to calculate the Q corresponding to each action a. total (s, a) value;

[0077] Action a is to adjust the workload of the equipment, Q total (s, a) is the total benefit of taking action a in state s;

[0078] Choose Q from all actions total (s, a) best action a * , the best action a is selected * The resource allocation instruction is converted into a specific control signal, and the corresponding resource allocation is performed according to the generated control signal;

[0079] Design an intelligent scheduling algorithm to allocate resources according to the actual needs of the park and define the objective function of the algorithm to minimize the total cost.

[0080]

[0081] Among them, J(P) is the minimum total cost, U i (P i ) is the i-th task P i The resource utilization under the condition, n is the number of tasks, P is the scheduling plan, α is the parameter for adjusting the steepness of the resource utilization curve, β is the parameter for adjusting the steepness of the cost curve, μ U is the average resource utilization, D(P) is the cost-adjusted sum of all tasks, and i is the task index;

[0082] Collect the status data of current resources and input the availability of resources into the intelligent scheduling algorithm to obtain the resource demand forecast value, which is expressed as:

[0083] R(t)=A(t)×(1+exp(-λ(Z(t)-θ))) -1 +∈(t);

[0084] Among them, R(t) is the predicted value of resource demand at time t, Z(t) is the linear combination of factors affecting resource demand at time t, ∈(t) is the random error term, λ is the adjustment parameter, θ is the baseline value of Z(t), and A(t) represents resource availability.

[0085] S3, set the deviation threshold. When the predicted value R(t)>, adjust the resource allocation through the intelligent scheduling algorithm and generate a new scheduling plan. The expression is:

[0086]

[0087] Among them, P new For the new scheduling plan, is an element in the set P, D f (P f ) is the fth resource unit P f The delay value, P f is the allocation of the f-th resource, u is the range of the sum of delays of all resource units, and f is the resource unit index;

[0088] Furthermore, when the resource demand forecast value is greater than the threshold, it indicates that the current resource allocation is insufficient and resource allocation needs to be adjusted. A new scheduling plan is calculated through an intelligent scheduling algorithm to ensure efficient and flexible resource utilization.

[0089] S4, according to the new scheduling plan, executing new resource allocation includes the following steps:

[0090] Based on the specific dispatch instructions generated by the selected best action, the dispatch instructions are sent to the operator and the collected data are input into the state vector;

[0091] Use anomaly detection algorithms to analyze the collected data and standardize the data for easy comparison and analysis. When the utilization of the equipment is higher than the historical average, the energy consumed by the equipment is higher than the historical average, and there is a deviation from the expected behavior, take remedial measures, and record the problems encountered during the execution that did not operate as expected, using a structured log format;

[0092] Collect various equipment utilization and energy consumption indicators during the execution process;

[0093] The weighted average formula is used to calculate the comprehensive score of each indicator;

[0094]

[0095] Among them, C s It is composed of multiple indicators. v is the actual value of the v index, ω ν is the importance weight of the νth indicator, ω j is the importance weight of the jth indicator, τ is the total number of indicators for comprehensive score calculation;

[0096] Use the deviation calculation formula to calculate the deviation between the comprehensive score of the indicator and the target comprehensive score. The expression is:

[0097]

[0098] Among them, C s is the comprehensive score, C target Give your target a comprehensive score;

[0099] When the deviation is large, new resource allocation needs to be performed;

[0100] Furthermore, the gap between the comprehensive score and the target value is evaluated to determine whether resource allocation needs to be adjusted, ensuring accurate calculation of deviations and improving the timeliness and rationality of resource allocation adjustments;

[0101] When the deviation is large, it indicates that there is a large gap between the current resource allocation and the target value, and a new scheduling plan needs to be regenerated.

[0102] S5, executing new resource allocation and generating feedback information includes the following steps:

[0103] Executing new resource allocation and generating feedback information includes the following steps:

[0104] Summarize the personnel situation, equipment operation status and environmental condition data during the execution process to form a detailed feedback report;

[0105] The actual workload of the equipment deviates from the expected workload;

[0106] The actual ambient temperature deviates from the expected temperature;

[0107] The actual flow of people deviates from the expected flow of people;

[0108] When the deviation is large, the scheduling plan needs to be adjusted and the scheduling process needs to be re-entered;

[0109] Furthermore, it detects whether there is a deviation between the actual workload of the device and the expected workload, and finds out whether the workload of the device meets expectations, and determines whether resource allocation needs to be adjusted;

[0110] Detect whether there is a deviation between the actual ambient temperature and the expected temperature, promptly find out whether the ambient temperature meets expectations, and determine whether the environmental control strategy needs to be adjusted to ensure the suitability of the ambient temperature and improve comfort and work efficiency;

[0111] Detect whether there is a deviation between the actual and expected crowd flow conditions, promptly discover whether the crowd flow conditions meet expectations, and determine whether the crowd flow management strategy needs to be adjusted to ensure the rationality of crowd flow management and avoid congestion and waste of resources;

[0112] When there is a large deviation between the actual value detected and the expected value, it is necessary to adjust the scheduling plan and re-enter the scheduling process to ensure dynamic adjustment of resource allocation, thereby improving resource utilization efficiency and response speed.

[0113] S6, updating the data set of the intelligent scheduling algorithm according to the feedback information, and optimizing the resource allocation scheme includes the following steps:

[0114] Extract execution results, deviation analysis, and problem records from feedback reports;

[0115] Retrain the smart scheduling model using updated data;

[0116] Introduce new feature variables, such as equipment maintenance history and personnel flow distribution, to improve the prediction accuracy of the model and add them to the state vector;

[0117] Use the optimized model to conduct a new round of simulation experiments in the simulation environment to evaluate the performance of the model on the new data set;

[0118] By comparing the differences between the simulation results and the actual execution results, the performance of the model was evaluated. The model performed better than the intelligent scheduling algorithm and was deployed in the actual environment. If it did not outperform the intelligent scheduling algorithm, it would continue to be adjusted and optimized.

[0119] This embodiment also provides an IOC-based intelligent operation management system, including: a data collection and preprocessing module, which collects equipment, personnel flow and data in the park and performs preprocessing;

[0120] Intelligent scheduling algorithm module, based on pre-processed data, designs intelligent scheduling algorithms to allocate resources, analyze the current resource allocation status of the park and generate new scheduling plans;

[0121] The resource allocation execution module executes new resource allocation according to the new scheduling plan;

[0122] A feedback information generation module generates feedback information according to the execution result;

[0123] The data set update and optimization module updates the data set of the intelligent scheduling algorithm based on feedback information and optimizes the resource allocation plan.

[0124] This embodiment also provides a computer device, which is suitable for the case based on the IOC intelligent operation management method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the IOC-based intelligent operation management method proposed in the above embodiment.

[0125] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0126] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the file encryption method proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0127] In summary, the present invention realizes data standardization by collecting equipment, personnel flow and data in the park and performing preprocessing, thereby providing reliable basic data for the intelligent scheduling algorithm, thereby improving the accuracy and reliability of scheduling decisions, and designs an intelligent scheduling algorithm based on the preprocessed data, dynamically adjusts the resource allocation strategy according to real-time data, and timely discovers and adjusts existing problems through analysis of the current resource allocation status, generates a new scheduling plan, and executes new resource allocation according to the new scheduling plan. The scheduling plan generated by the intelligent scheduling algorithm is converted into a specific control signal, and resource allocation is adjusted through actual operation to ensure that resources are allocated according to the optimal plan. Feedback information is generated by the execution result, and the data set of the intelligent scheduling algorithm is updated according to the feedback information. The algorithm is continuously optimized to ensure continuous improvement of the resource allocation plan.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An IOC-based intelligent operation management method, characterized in that: include, Collect equipment, personnel flow and data in the park and perform pre-processing; Based on the preprocessed data, design intelligent scheduling algorithms to allocate resources, analyze the current resource allocation status of the park and generate new scheduling plans; Execute new resource allocation according to the new scheduling plan; Generate feedback information based on the execution results; Update the data set of the intelligent scheduling algorithm based on the feedback information and optimize the resource allocation plan.

2. The IOC-based intelligent operation management method according to claim 1, characterized in that: Collecting equipment, people flow and data in the park and preprocessing them include the following steps: Collect data on the working status of equipment in the park, resource availability, usage frequency and energy consumption, collect data on the time, path and stay area of ​​people entering and leaving, collect data on temperature, humidity and light intensity, and clean, format and deduplicate the collected data.

3. The IOC-based intelligent operation management method according to claim 2, characterized in that: Based on the preprocessed data, an intelligent scheduling algorithm is designed to allocate resources, analyze the current resource allocation status of the park and generate a new scheduling plan, including the following steps: The equipment operation status, human flow status and data are obtained from the preprocessed data to form a state vector and a data set. The architecture of the DQN model is defined. The preprocessed data is selected from the data set to train the DQN model. The trained DQN model is used to calculate the Q corresponding to each action a. total (s, a) value; Action a is to adjust the workload of the equipment, Q total (s, a) is the total benefit of taking action a in state s; Choose Q from all actions total (s, a) best action a * , the best action a is selected * The resource allocation instruction is converted into a specific control signal, and the corresponding resource allocation is performed according to the generated control signal; Design an intelligent scheduling algorithm to allocate resources according to the actual needs of the park and define the objective function of the algorithm to minimize the total cost. Among them, J(P) is the minimum total cost, U i (P i ) is the i-th task P i The resource utilization under the condition, n is the number of tasks, P is the scheduling plan, α is the parameter for adjusting the steepness of the resource utilization curve, β is the parameter for adjusting the steepness of the cost curve, μ U is the average resource utilization, D(P) is the cost-adjusted sum of all tasks, and i is the task index; Collect the status data of current resources and input the availability of resources into the intelligent scheduling algorithm to obtain the resource demand forecast value, which is expressed as: R(t)=A(t)×(1+exp(-λ(Z(t)-θ))) -1 +∈(t); Among them, R(t) is the predicted value of resource demand at time t, Z(t) is the linear combination of factors affecting resource demand at time t, ∈(t) is the random error term, λ is the adjustment parameter, θ is the baseline value of Z(t), and A(t) represents resource availability.

4. The IOC-based intelligent operation management method according to claim 3, characterized in that: Set the deviation threshold. When the predicted value R(t)>, adjust the resource allocation through the intelligent scheduling algorithm and generate a new scheduling plan. The expression is: Among them, P new is a new scheduling plan, P∈P is an element in the set P, D f (P f ) is the fth resource unit P f The delay value, P f is the allocation of the f-th resource, u is the range of the sum of delays of all resource units, and f is the resource unit index.

5. The IOC-based intelligent operation management method according to claim 4, characterized in that: According to the new scheduling plan, executing new resource allocation includes the following steps: Based on the specific dispatch instructions generated by the selected best action, the dispatch instructions are sent to the operator and the collected data are input into the state vector; Use anomaly detection algorithms to analyze the collected data and standardize the data for easy comparison and analysis. When the utilization of the equipment is higher than the historical average, the energy consumed by the equipment is higher than the historical average, and there is a deviation from the expected behavior, take remedial measures, and record the problems encountered during the execution that did not operate as expected, using a structured log format; Collect various equipment utilization and energy consumption indicators during the execution process; The weighted average formula is used to calculate the comprehensive score of each indicator; Among them, C s It is composed of multiple indicators. v is the actual value of the v index, ω v is the importance weight of the vth indicator, ω j is the importance weight of the jth indicator, τ is the total number of indicators for comprehensive score calculation; Use the deviation calculation formula to calculate the deviation between the comprehensive score of the indicator and the target comprehensive score. The expression is: Among them, C s is the comprehensive score, C target Give your target a comprehensive score; When the deviation is large, new resource allocation needs to be performed.

6. The IOC-based intelligent operation management method according to claim 5, characterized in that: Executing new resource allocation and generating feedback information includes the following steps: Executing new resource allocation and generating feedback information includes the following steps: Summarize the personnel situation, equipment operation status and environmental condition data during the execution process to form a detailed feedback report; The actual workload of the equipment deviates from the expected workload; The actual ambient temperature deviates from the expected temperature; The actual flow of people deviates from the expected flow of people; When the deviation is large, the scheduling plan needs to be adjusted and the scheduling process needs to be re-entered.

7. The IOC-based intelligent operation management method according to claim 6, characterized in that: The data set of the intelligent scheduling algorithm is updated according to the feedback information, and the resource allocation scheme is optimized, including the following steps: Extract execution results, deviation analysis, and problem records from feedback reports; Retrain the smart scheduling model using updated data; Introduce new feature variables, such as equipment maintenance history and personnel flow distribution, to improve the prediction accuracy of the model and add them to the state vector; Use the optimized model to conduct a new round of simulation experiments in the simulation environment to evaluate the performance of the model on the new data set; By comparing the differences between the simulation results and the actual execution results, the performance of the model was evaluated. The model performed better than the intelligent scheduling algorithm and was deployed in the actual environment. If it did not outperform the intelligent scheduling algorithm, it would continue to be adjusted and optimized.

8. An IOC-based intelligent operation management system, based on the IOC-based intelligent operation management method according to any one of claims 1 to 7, characterized in that: Including, data collection and preprocessing module, which collects equipment, personnel flow and data in the park and performs preprocessing; Intelligent scheduling algorithm module, based on pre-processed data, designs intelligent scheduling algorithms to allocate resources, analyze the current resource allocation status of the park and generate new scheduling plans; The resource allocation execution module executes new resource allocation according to the new scheduling plan; A feedback information generation module generates feedback information according to the execution result; The data set update and optimization module updates the data set of the intelligent scheduling algorithm based on feedback information and optimizes the resource allocation plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the IOC-based intelligent operation management method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the IOC-based intelligent operation management method according to any one of claims 1 to 7 are implemented.

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