Iot-based park intelligent enterprise management system

Abstract

The application discloses a park intelligent enterprise management system based on Internet of Things and particularly relates to the technical field of enterprise equipment management; the application realizes accurate matching of research and development tasks and equipment through multi-dimensional data acquisition and scientific evaluation logic, completely changes the mode of traditional experience-dependent resource allocation, and after core information such as task type, priority, required equipment type and completion time limit is called, candidate equipment is first screened according to the equipment type, then a comprehensive score is obtained by quantitatively calculating a load capacity coefficient, an operation efficiency coefficient and an energy consumption efficiency coefficient, meanwhile, a corresponding comprehensive threshold score is set according to the task priority, the equipment meeting the requirements is screened out, a stability evaluation coefficient is further calculated by combining the number of faults, the estimated total energy consumption, the load and the temperature standard deviation, and finally, the equipment with the highest coefficient is selected as the target equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of enterprise equipment management, more particularly, the present application relates to a park intelligent enterprise management system based on Internet of Things. BACKGROUND

[0002] With the rapid development of park economy, various high-tech enterprises gather in the park, and enterprise R&D activities are increasingly frequent. The types and quantities of R&D equipment are growing explosively. As the key infrastructure for enterprises to carry out core technology research and product iteration optimization, the stability of the operation state, the rationality of the load and the resource utilization efficiency of the R&D equipment directly determine the progress and quality of the enterprise R&D tasks, and are also the core factors affecting the overall industrial innovation efficiency of the park.

[0003] However, the current park enterprises still rely on the traditional management mode of manual inspection record and experience-based scheduling allocation in the field of R&D equipment management, which is difficult to adapt to the fine, real-time and intelligent needs of equipment management in the modern R&D scene. There are the following outstanding problems:

[0004] Resource scheduling lacks scientific basis. Due to the lack of effective data collection and analysis means, when allocating R&D tasks, only the past experience of management personnel can be relied on. The intelligent degree of task allocation management is low, and it is not possible to compare the performance of the same type of equipment to select the best equipment that meets the requirements of the R&D task. For example, assigning a high-precision, high-priority R&D task to a device with weak load capacity and low efficiency in the same type of R&D equipment not only may affect the accuracy of test data due to insufficient device performance, but also may increase the risk of device failure, resulting in serious waste of R&D resources.

[0005] Therefore, a park intelligent enterprise management system based on Internet of Things is proposed. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a park intelligent enterprise management system based on Internet of Things.

[0007] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0008] The park intelligent enterprise management system based on Internet of Things comprises the following modules:

[0009] The task interaction module is used to retrieve R&D task information, and the R&D task information includes task type, task priority, task required equipment type and task completion time limit;

[0010] The Internet of Things sensing module is used to collect the load data and performance parameters of the enterprise R&D equipment in the park: the load data includes current and temperature; the performance parameters include load capacity coefficient, operation efficiency coefficient and energy consumption performance coefficient;

[0011] The device matching module is configured to filter out candidate devices of a corresponding type according to a required device type of the R&D task, and to construct a device matching evaluation logic in combination with a task priority, a task completion time limit, and performance parameters of the candidate devices, so as to filter out an optimal device from the candidate devices;

[0012] The decision analysis module is configured to receive the optimal device information and the R&D task information, and to distribute the R&D task information to a control terminal of the optimal device;

[0013] The decision analysis module is further configured to receive a load warning level of the optimal device, to selectively perform load adjustment on the optimal device, or to re-trigger device matching signaling to be sent to the device matching module to select a backup device to undertake the R&D task;

[0014] The load prediction module is configured to receive load data in a running process of the optimal device, to dynamically analyze the load data, to determine a load warning level, and to send the load warning level to the decision analysis module; the load warning level includes a general warning level and a serious warning level.

[0015] The load prediction module is configured to use a weighted calculation logic in combination with a highest capability load of the optimal device and a preset normal running performance temperature, to obtain a load warning coefficient of the optimal device in a set time zone, by using a load performance value, a load additional value, and a load fluctuation coefficient.

[0016] If the load warning coefficient is within a warning coefficient range, the general warning level is determined; if the load warning coefficient is higher than the warning coefficient range, the serious warning level is determined.

[0017] Specifically, the calculation logic of the performance parameters is as follows:

[0018] The time length consumed by single test of each group of candidate devices is counted, the total number of required tests in the R&D task information is multiplied by the time length consumed by single test of each group of candidate devices, to obtain an estimated total time consumed by each group of candidate devices for executing the R&D task;

[0019] The running efficiency coefficient of the candidate device is obtained by performing ratio calculation with the estimated total time consumed by the candidate device as a denominator and the task completion time limit as a numerator;

[0020] The load capability coefficient of the candidate device is obtained by performing ratio calculation with a required load of the task in the R&D task information as a denominator and the highest capability load of the candidate device as a numerator;

[0021] The energy consumption efficiency coefficient of the candidate device is obtained by performing ratio calculation with an energy consumption average value of each group of candidate devices as a numerator and an actual energy consumption value of the candidate device as a denominator.

[0022] Specifically, the device matching evaluation logic is constructed in combination with the task priority, the task completion time limit, and the performance parameters of the candidate devices.

[0023] The load capacity coefficient, the operation efficiency coefficient and the energy consumption performance coefficient of each group of candidate devices are comprehensively processed by using the device matching evaluation logic respectively, and the comprehensive scores of each group of candidate devices are obtained, and the candidate devices with the comprehensive scores higher than the comprehensive threshold score are selected as the qualified devices;

[0024] The comprehensive scores, the failure times, the estimated total energy consumption, the load standard deviation and the temperature standard deviation of each group of qualified devices are comprehensively processed, and the stability evaluation coefficients of each group of qualified devices are obtained, and the qualified devices with higher stability evaluation coefficients are selected as the target devices.

[0025] Specifically, the setting logic of the comprehensive threshold score is:

[0026] The task priority includes high priority, medium priority and low priority; the mapping rule between the task priority and the comprehensive threshold score is set, that is, the high priority, the medium priority and the low priority correspond to the comprehensive threshold score of a group of devices respectively, and the comprehensive threshold score of the current research and development task for screening devices is determined.

[0027] Specifically, the specific calculation logic of the failure times, the estimated total energy consumption, the load standard deviation and the temperature standard deviation is:

[0028] Taking the current time point as the starting point, the failure times of each group of qualified devices in the set time zone before the starting point are counted; the estimated total energy consumption is obtained by multiplying the estimated total time of each group of qualified devices by the actual energy consumption value; and the load standard deviation and the temperature standard deviation in the last running process of each group of qualified devices are retrieved.

[0029] Specifically, the calculation logic of the load performance value and the load additional value is:

[0030] The time interval of load dynamic analysis is set, and the load data of the device in the set time zone is intercepted after the set time interval is reached;

[0031] The current and temperature of the best device at each time point in the set time zone are both calculated by the average value to obtain the load performance value and the load additional value.

[0032] Specifically, the calculation logic of the load fluctuation coefficient is:

[0033] The set time zone is divided into the first half time zone and the second half time zone by the middle time point, the load performance value of the best device corresponding to the first half time zone and the second half time zone is retrieved, the load performance value of the second half time zone is the numerator, and the load performance value of the first half time zone is the denominator to perform ratio calculation, and the trend change coefficient is obtained;

[0034] The trend change coefficient is converted into a load fluctuation coefficient by using a mapping rule between the trend change coefficient and the load fluctuation coefficient, that is, each group of coefficient intervals corresponding to the preset trend change coefficient, and each group of coefficient intervals corresponds to a group of load fluctuation coefficients, the trend change coefficient is matched with the corresponding coefficient interval, and the load fluctuation coefficient of the best equipment in the set time zone is determined.

[0035] Specifically, the load of the best equipment is adjusted selectively by receiving the load early warning level of the best equipment, and specifically:

[0036] If the load early warning level is a general early warning level, the management personnel are sent a load fine-tuning signaling, and the load of the best equipment is adjusted downward, and if the load early warning coefficient in the next set time zone is still in the early warning coefficient range, the load early warning level is upgraded to a serious early warning level.

[0037] If the load early warning level is a serious early warning level, a device matching signaling is triggered and sent to a device matching module, and the device with the second highest stable evaluation coefficient is selected as a standby device.

[0038] The technical effects and advantages of the present application are as follows:

[0039] (1) Through multi-dimensional data acquisition and scientific evaluation logic, the accurate matching of R&D tasks and equipment is realized, and the traditional mode of relying on experience to allocate resources is completely changed. After the core information such as task type, priority, required equipment type and completion time limit is retrieved, the candidate equipment is first screened according to the equipment type, then the load capacity coefficient, the operation efficiency coefficient and the energy consumption efficiency coefficient are calculated quantitatively to obtain the comprehensive score, at the same time, the corresponding comprehensive threshold score is set according to the task priority, the equipment that meets the requirements is screened out, and then the stable evaluation coefficient is calculated by combining the fault frequency, the estimated total energy consumption, the load and the temperature standard deviation, and finally the equipment with the highest coefficient is selected as the target equipment.

[0040] (2) The load data is intercepted according to the set time interval, the load performance value and the load additional value are calculated, and the load fluctuation coefficient is obtained by mapping the trend change coefficient, and then the load early warning coefficient is calculated by combining the highest capacity load and the normal operation performance temperature, so as to realize the risk early warning of the load change of the equipment.

[0041] (3) By determining the general or serious early warning level, when the general early warning is triggered, the fine-tuning signaling is sent to the management personnel to adjust the load downward, and if the early warning is not removed, the serious early warning is upgraded, and the standby equipment dispatching is immediately triggered, so that the early warning can be captured and triggered in advance, and the load fine-tuning or standby equipment transfer can avoid the interruption of R&D caused by equipment failure. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The present application is based on the principle diagram of the park intelligent enterprise management system of the Internet of Things. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0044] As shown in the figure, the park intelligent enterprise management system based on Internet of Things includes the following modules: Figure 1

[0045] The task interaction module is used to establish a communication connection with the park enterprise R&D task management system, to call R&D task information, and to transmit the called R&D task information to the device matching module, wherein the R&D task information includes task type, task priority, task required device type, and task completion time limit.

[0046] The Internet of Things sensing module is used to deploy Internet of Things sensing devices for the park enterprise R&D devices, to collect load data and performance parameters of the park enterprise R&D devices, and to transmit the collected load data to the load prediction module, wherein the load data includes current and temperature, and the performance parameters include load capacity coefficient, operation efficiency coefficient, and energy consumption performance coefficient.

[0047] The sensing devices include current sensors and temperature sensors, which are respectively installed at corresponding monitoring parts of the R&D devices, and are used to collect current data and temperature data of the R&D devices as a part of real-time load data.

[0048] The received load data is subjected to noise filtering, data normalization, and missing value filling processing, to remove interference information in the data, to unify the data format, to complete the missing data, and then to transmit the preprocessed real-time load data to the load prediction module.

[0049] The device matching module is used to filter out candidate devices of corresponding types according to the required device type of the R&D task, to construct a device matching evaluation logic in combination with the task priority, the task completion time limit, and the performance parameters of the candidate devices, to filter out the best device from the candidate devices, and to send the best device information and the corresponding R&D task information to the decision analysis module.

[0050] Specifically,

[0051] The task priority includes high priority, medium priority, and low priority; the task priority is marked in advance by technical personnel according to the R&D task information.

[0052] ​Mapping rules between task priority and comprehensive threshold score are set, that is, high priority, medium priority and low priority correspond to a set of comprehensive threshold scores of devices respectively, and the comprehensive threshold score of the device for screening the current R&D task is determined;

[0053] High priority comprehensive threshold score > medium priority comprehensive threshold score > low priority comprehensive threshold score;

[0054] According to the test steps in the R&D task information, single test signaling is sent to each group of candidate devices, the time consumed by single test of each group of candidate devices is counted, the total required test times in the R&D task information is multiplied by the time consumed by single test of each group of candidate devices to obtain the estimated total time consumed by each group of candidate devices to execute the R&D task;

[0055] The running efficiency coefficient of each group of candidate devices is obtained by ratio calculation of the estimated total time consumed as the denominator and the task completion time limit as the numerator in turn; the estimated total time consumed and the task completion time limit are converted into minute units before ratio calculation;

[0056] The required load of the task in the R&D task information is called as the denominator, and the highest capability load of each group of candidate devices is obtained as the numerator to perform ratio calculation in turn to obtain the load capability coefficient of each group of candidate devices;

[0057] The energy consumption average of each group of candidate devices is taken as the numerator, and the actual energy consumption value of each group of candidate devices is taken as the denominator to perform ratio calculation in turn to obtain the energy consumption efficiency coefficient of each group of candidate devices;

[0058] The load capability coefficient, the running efficiency coefficient and the energy consumption performance coefficient of each group of candidate devices are comprehensively processed by using the device matching evaluation logic to obtain the comprehensive score of each group of candidate devices;

[0059] The device matching evaluation logic specifically processes as follows: ; wherein is the comprehensive score, respectively represent the load capability coefficient, the running efficiency coefficient and the energy consumption performance coefficient, respectively are preset weight coefficients;

[0060] For example, the R&D task information is "new energy battery capacity cycle test", the task priority is high priority, and the candidate device information (selecting four same type "battery cycle test devices" as candidates);

[0061] Device A: highest load 100A, actual energy consumption 2.2;

[0062] Device B: highest load 90A, actual energy consumption 2.6;

[0063] Device C: highest load 85A, actual energy consumption 2.3;

[0064] Device D: maximum load 75A, actual energy consumption 2.4;

[0065] According to the "task priority and comprehensive threshold score mapping rule", the current task priority is high priority, and the corresponding comprehensive threshold score is determined;

[0066] According to the R&D task test steps, single test signaling is sent to the four candidate devices respectively, and the time (unit: minutes) taken by each device to complete the single test is recorded;

[0067] Running efficiency coefficient = task completion time limit (minutes) ÷ estimated total time (minutes);

[0068] Load capacity coefficient = candidate device maximum load ÷ task required load;

[0069] Energy consumption efficiency coefficient = energy consumption average ÷ candidate device actual energy consumption value;

[0070] Calculate the comprehensive score of the four devices = load capacity coefficient × 0.4 + running efficiency coefficient × 0.4 + energy consumption efficiency coefficient × 0.2.

[0071] Screen the candidate devices with a comprehensive score higher than the comprehensive threshold score as the conforming devices;

[0072] Take the current time point as the starting point, and count the number of failures of each group of conforming devices within the set time zone before the starting point;

[0073] Multiply the estimated total time of each group of conforming devices by the actual energy consumption value to obtain the estimated total energy consumption; during the calculation process, convert the estimated total time from minutes to hours and divide by 60;

[0074] At the same time, call the load standard deviation and temperature standard deviation of each group of conforming devices in the last running process; that is, count the load value and temperature value of the device at each time point in the last running process, and calculate the load standard deviation and temperature standard deviation after the calculation;

[0075] After normalizing the comprehensive score, the number of failures, the estimated total energy consumption, the load standard deviation, and the temperature standard deviation of each group of conforming devices, use the device matching evaluation logic for comprehensive processing to obtain the stability evaluation coefficient of each group of conforming devices, and select the conforming device with a higher stability evaluation coefficient as the target device;

[0076] Comprehensive processing using the device matching evaluation logic: ; Wherein is the stability evaluation coefficient, respectively represent the normalized failure frequency, estimated total energy consumption, load standard deviation, and temperature standard deviation, are respectively preset weight coefficients;

[0077] For example, taking the new energy battery capacity cycle test as an example, the corresponding comprehensive threshold score according to the task priority (high priority) is compared and screened to select the qualified equipment;

[0078] The number of faults of the qualified equipment (set time zone: the past 30 days from the current time) is counted, and the number of faults of each qualified equipment in the past 30 days is counted from the current time as the starting point;

[0079] The estimated total energy consumption (kW·h) = estimated total time (hours) x actual energy consumption value (kW / h);

[0080] The load value and temperature value of the last running process of each qualified equipment are retrieved, and the temperature and load at each time point are calculated using the standard deviation formula;

[0081] In order to eliminate the influence of different index data magnitude differences, the "min-max normalization" method (normalized value = (original value-minimum value) / (maximum value-minimum value)) is used to process "comprehensive score, fault times, estimated total energy consumption, load standard deviation, temperature standard deviation";

[0082] The decision analysis module is used to receive the best equipment information and the research and development task information, and to send the research and development task information to the control terminal of the best equipment, and to send the task start scheduling instruction to the best equipment, to control the best equipment to start running according to the research and development task requirements, and to feed back the scheduling instruction execution situation to the equipment matching decision module;

[0083] It is also used to receive the load warning level of the best equipment, and to selectively adjust the load of the best equipment, or to re-trigger the equipment matching signaling to send to the equipment matching module to select a standby equipment to undertake the research and development task;

[0084] Specifically:

[0085] If the load warning level is a general warning level, send the load fine-tuning signaling to the management personnel to reduce the load of the best equipment, and if the load warning coefficient is still in the warning coefficient range in the next set time zone, upgrade to a serious warning level;

[0086] If the load warning level is a serious warning level, trigger the equipment matching signaling to send to the equipment matching module to select the second highest stable evaluation coefficient as a standby equipment;

[0087] The load prediction module is used to receive the load data in the running process of the best equipment, to dynamically analyze the load data, to judge the load warning level and to send it to the decision analysis module; The load warning level is a general warning level and a serious warning level;

[0088] During the execution of the R&D task, the Internet of Things sensing module is relied on to realize continuous collection and real-time transmission of load data, providing a data basis for risk analysis:

[0089] Collection frequency setting: Taking the battery capacity cycle test as an example, considering that load fluctuations in the “battery capacity cycle test” may affect test accuracy, the collection frequency is set to 1 minute / time (higher than 10 minutes / time before task execution, improving dynamic monitoring sensitivity);

[0090] Collection data type: Through the current sensors and temperature sensors already deployed on the device, two types of core load data are collected, namely real-time current data (corresponding to load capacity); real-time temperature data (reflecting the device running state).

[0091] Specifically:

[0092] Set the time interval for load dynamic analysis, and intercept the load data of the device within the set time zone after reaching the set time interval;

[0093] For the current and temperature of the best device at each time point within the set time zone, the average value is calculated to obtain the load performance value and the load additional value;

[0094] Using the load performance value, the load additional value, and the load fluctuation coefficient, combined with the highest capacity load of the best device and the preset normal running performance temperature, the load warning coefficient of the best device within the set time zone is obtained using the weighted calculation logic; the normal running performance temperature can be obtained by collecting the temperature values of the device during the historical normal running process and calculating the average value as the normal running performance temperature;

[0095] Load warning coefficient obtained by weighted calculation logic: ; wherein represents the load warning coefficient; represents the load performance value, the load additional value, the highest capacity load, and the normal running performance temperature, respectively; are preset weight coefficients, respectively; represents the load fluctuation coefficient;

[0096] The preset load warning coefficient corresponds to a warning coefficient range, if the load warning coefficient is within the warning coefficient range, the load warning level is determined to be a general warning level, if the load warning coefficient is higher than the warning coefficient range, the load warning level is determined to be a serious warning level;

[0097] The calculation process of the load fluctuation coefficient is:

[0098] The time zone is divided into a first half time zone and a second half time zone at a middle time point, the load performance value of the best device corresponding to the first half time zone and the second half time zone is called, and a ratio calculation is performed with the load performance value of the second half time zone as a numerator and the load performance value of the first half time zone as a denominator to obtain a trend change coefficient; if the trend change coefficient is greater than 1, it indicates that the device load shows an upward trend compared with the first half time zone and the second half time zone, and the load warning coefficient needs to be adjusted upward.

[0099] The trend change coefficient is converted into a load fluctuation coefficient by using a mapping rule between the trend change coefficient and the load fluctuation coefficient, that is, each group of coefficient intervals corresponding to the preset trend change coefficient, and each group of coefficient intervals corresponds to a group of load fluctuation coefficients; the load fluctuation coefficient is set to be in the range of 0.891-1.217, and the higher the trend change coefficient, the higher the possibility of matching 1.217;

[0100] The trend change coefficient is matched with the corresponding coefficient interval to determine the load fluctuation coefficient of the best device in the set time zone.

[0101] For example, the mapping principle is that the higher the trend change coefficient, the closer the corresponding load fluctuation coefficient to 1.217; the lower the trend change coefficient (but ≥0.75, lower than that is regarded as abnormal), the closer the corresponding load fluctuation coefficient to 0.861;

[0102] According to the physical characteristics (current change rate ≤1.2 A / min) of the device load and historical data statistics, the trend change coefficient is divided into five intervals, each interval covers a "reasonable trend range" (0.75-1.45), and the interval is too wide to cause insufficient mapping accuracy, and the specific division is as follows:

[0103] Interval 1 (0.75-0.85), corresponding to a load fluctuation coefficient of 0.861;

[0104] Interval 2 (0.85-0.95), corresponding to a load fluctuation coefficient of 0.973;

[0105] Interval 1 (0.95-1.05), corresponding to a load fluctuation coefficient of 1.085;

[0106] Interval 1 (1.05-1.25), corresponding to a load fluctuation coefficient of 1.149;

[0107] Interval 1 (1.25-1.45), corresponding to a load fluctuation coefficient of 1.217.

[0108] The above formulas are all dimensionless values calculated, and specific dimensionless can be standardized and other means, and will not be described here, the formula is obtained by collecting a large amount of data to simulate the formula of the nearest real situation, the preset parameters in the formula, such as weight coefficients a1, a2, a3, etc. are configurable parameters, and their specific values can be determined and optimized by a person skilled in the art according to the actual application scene, the expected performance characteristics, and the relative importance of each factor through experience setting, experimental testing, simulation or machine learning.

[0109] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center and the like containing one or more available medium sets. The available medium can be a magnetic medium (such as a floppy disk, an ATA hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state ATA hard disk.

[0110] It should be understood that in various embodiments of the present application, the size of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0111] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0112] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. The division of the units is merely logical function division. There can be other division manners in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0113] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, and can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0114] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0115] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product in essence or the part of the technical solutions that make contributions to the prior art or part of the technical solutions. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile ATA hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0116] The above is merely specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A smart enterprise management system for industrial parks based on the Internet of Things, characterized in that: Includes the following modules: The task interaction module is used to retrieve R&D task information, which includes task type, task priority, type of equipment required for the task, and task completion time limit. The IoT sensing module is used to collect load data and performance parameters of R&D equipment of enterprises in the park: load data includes current and temperature; performance parameters include load capacity coefficient, operating efficiency coefficient, and energy consumption performance coefficient. The calculation logic for performance parameters is as follows: The time consumed in a single test of each group of candidate devices is calculated. The total number of tests required in the R&D task information is multiplied by the time consumed in a single test of each group of candidate devices to obtain the estimated total time for each group of candidate devices to perform the R&D task. The operating efficiency coefficient of the candidate equipment is obtained by calculating the ratio between the estimated total time of the candidate equipment as the denominator and the task completion time limit as the numerator. The load required for the task in the R&D task information is used as the denominator, and the highest capacity load of the candidate equipment is used as the numerator to calculate the ratio and obtain the load capacity coefficient of the candidate equipment. The average energy consumption of each group of candidate devices is calculated as the numerator, and the actual energy consumption of the candidate devices is calculated as the denominator. The ratios are then used to obtain the energy consumption performance coefficient of the candidate devices. The equipment matching module is used to filter out candidate equipment of the corresponding type according to the equipment type required by the R&D task. Then, it combines the task priority, task completion time limit and performance parameters of the candidate equipment to build equipment matching evaluation logic and select the best equipment from the candidate equipment. The specific logic for equipment matching evaluation is as follows: Task priorities include high priority, medium priority, and low priority; a mapping rule is set between task priorities and comprehensive threshold scores, that is, high priority, medium priority, and low priority each correspond to a set of comprehensive threshold scores of equipment, and the comprehensive threshold scores of equipment to be selected for the current R&D task are determined. The load capacity coefficient, operating efficiency coefficient, and energy consumption performance coefficient of each group of candidate equipment are comprehensively processed to obtain the comprehensive score of each group of candidate equipment. Candidate equipment with a comprehensive score higher than the comprehensive threshold score is selected as qualified equipment. The overall score, number of failures, estimated total energy consumption, load standard deviation and temperature standard deviation of each group of compliant equipment are comprehensively processed to obtain the stability evaluation coefficient of each group of compliant equipment. The compliant equipment with the highest stability evaluation coefficient is selected as the best equipment. The decision analysis module is used to receive optimal equipment information and R&D task information, and to send the R&D task information to the control terminal of the optimal equipment. It is also used to receive the load warning level of the best equipment, selectively adjust the load of the best equipment, or re-trigger the equipment matching signaling to send to the equipment matching module to select the backup equipment to undertake the research and development task. The load prediction module receives load data during optimal equipment operation, performs dynamic analysis on the load data, determines the load warning level, and sends it to the decision analysis module; the load warning level is divided into general warning level and severe warning level. By using load performance values, load added values, and load fluctuation coefficients, combined with the highest capacity load of the optimal equipment and the preset normal operating temperature, a weighted calculation logic is used to obtain the load warning coefficient of the optimal equipment within a set time zone. The calculation logic for load performance value and load added value is as follows: Set the time interval for dynamic load analysis, and capture the load data of the device within the set time zone after the set time interval is reached; The load performance value and load additional value are obtained by averaging the current and temperature of the optimal device at each time point within the set time zone. The calculation logic for the load fluctuation coefficient is as follows: Divide the set time zone into the first half and the second half at the midpoint of time. Retrieve the load performance values ​​of the best equipment for the first half and the second half of time respectively. Use the load performance value of the second half of time as the numerator and the load performance value of the first half of time as the denominator to calculate the ratio and obtain the trend change coefficient. By utilizing the mapping rule between the trend change coefficient and the load fluctuation coefficient, the trend change coefficient is transformed into the load fluctuation coefficient; If the load warning coefficient is within the warning coefficient range, it is determined to be a general warning level; if it is higher than the warning coefficient range, it is determined to be a severe warning level.

2. The IoT-based intelligent enterprise management system for industrial parks according to claim 1, characterized in that, The specific calculation logic for the number of failures, estimated total energy consumption, load standard deviation, and temperature standard deviation is as follows: Starting from the current time point, count the number of failures of each group of compatible devices within the set time zone before the starting point; multiply the estimated total time consumption of each group of compatible devices by the actual energy consumption value to obtain the estimated total energy consumption; at the same time, retrieve the load standard deviation and temperature standard deviation of each group of compatible devices during the last operation.

3. The IoT-based intelligent enterprise management system for industrial parks according to claim 1, characterized in that, The specific transformation process of the mapping rule is as follows: The system presets the coefficient intervals corresponding to the trend change coefficients, and each coefficient interval corresponds to a set of load fluctuation coefficients. The trend change coefficients are matched with the corresponding coefficient intervals to determine the optimal load fluctuation coefficient for the equipment in the set time zone.

4. The IoT-based intelligent enterprise management system for industrial parks according to claim 1, characterized in that, The load warning level for the receiving optimal device selectively adjusts the load of the optimal device, specifically as follows: If the load warning level is a general warning level, a load fine-tuning signal will be sent to the management personnel to reduce the optimal equipment load. If the load warning coefficient is still within the warning coefficient range in the next set time zone, it will be upgraded to a severe warning level. If the load warning level is a severe warning level, a device matching signal is triggered and sent to the device matching module to select the device with the second highest stability evaluation coefficient as the backup device.

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