Highway electromechanical control system based on big data cloud service

Abstract

The present invention relates to the field of control technology, and in particular to a highway electromechanical control system based on big data cloud services. The system includes: a road condition severity determination module, which is used to determine the severity of the road condition; a stability coefficient calculation module, which is used to calculate the stability coefficient corresponding to each subsystem; an error degree coefficient determination module, which is used to correct the stability coefficient corresponding to each subsystem according to the severity of the road condition, and obtain the error degree coefficient corresponding to each subsystem; a proportional gain component correction module, which is used to correct the proportional gain component in the PID controller to obtain the target proportional gain component; an electric power adjustment module, which is used to adjust the electric power according to the target proportional gain component to compensate for the electric power error. Through this system, the control errors of each subsystem can be adaptively corrected, the power supply logic of each subsystem can be optimized, and the operation efficiency, safety and user experience of the highway can be improved.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to a highway electromechanical control system based on big data cloud services. Background Art

[0002] The highway electromechanical control system is an integrated control system for managing and monitoring various electromechanical equipment and systems in highway operation. These systems are usually composed of various sensors, actuators, controllers and data processing equipment, and are integrated and managed through computer networks to improve the operating efficiency, safety and user experience of highways.

[0003] The highway electromechanical control system based on big data cloud services can realize real-time monitoring of various parts of the highway through cloud services, and analyze various collected data, such as traffic flow, so as to provide data support for optimizing traffic management and equipment operation.

[0004] However, in the traditional highway electromechanical control system based on big data cloud services, when the control system is affected by external factors such as the external environment, resulting in the integrated control system being unable to accurately control the power consumption of various electromechanical system blocks (such as traffic signal control systems, road lighting systems, electronic toll collection systems, monitoring and safety systems, etc.), it will cause errors in the regulation system, which will in turn affect the normal operation of various system blocks, resulting in reduced highway operation efficiency, safety and user experience. Therefore, how to accurately and adaptively adjust and correct the errors generated by the highway electromechanical control system in harsh environments when integrating and controlling various system blocks is an important issue facing the highway electromechanical control system. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a highway electromechanical control system based on big data cloud services. The technical solutions adopted are as follows:

[0006] The present invention provides a highway electromechanical control system based on big data cloud service, the system comprising:

[0007] A road condition severity determination module is used to determine the severity of the road condition based on the unit time traffic volume of the expressway at a target time, and the actual operation data and expected operation data of each subsystem in the expressway electromechanical control system at the target time;

[0008] A stability coefficient calculation module, used to calculate the stability coefficient corresponding to each subsystem in the highway electromechanical control system according to the degree of correlation between the road condition severity set and the operation data set of each subsystem at multiple historical moments, and the disorder degree of the operation data of each subsystem in the time period corresponding to the target moment;

[0009] An error degree coefficient determination module is used to correct the stability coefficient corresponding to each subsystem according to the severity of the road condition, and obtain the error degree coefficient corresponding to each subsystem in the highway electromechanical control system;

[0010] A proportional gain component correction module, used to correct the proportional gain component in the PID controller used for electric power regulation according to the error degree coefficient, and obtain a target proportional gain component corresponding to each subsystem;

[0011] The power consumption regulating module is used to regulate the power consumption of the corresponding subsystems in the highway electromechanical control system according to the target proportional gain component to compensate for the power consumption error of each subsystem.

[0012] In some embodiments, the road condition severity determination module is specifically used to:

[0013] Calculate the average value of the difference between the actual operation data and the expected operation data of all subsystems at the target time to obtain the first parameter;

[0014] Performing an exponential operation on the vehicle flow per unit time corresponding to the target time as an index to obtain a second parameter, wherein the value of the second parameter is negatively correlated with the vehicle flow per unit time;

[0015] The first parameter is multiplied by the second parameter to obtain the severity of the road condition corresponding to the target time.

[0016] In some embodiments, the expected operation data is obtained based on the following method:

[0017] Performing time series analysis on the historical operation data corresponding to each subsystem to identify abnormal data, and determining the normal operation data of each subsystem in the electromechanical control system based on the abnormal data;

[0018] The normal operating data corresponding to each subsystem is obtained, and the average value of the normal operating data is calculated to obtain the expected operating data corresponding to each subsystem.

[0019] In some embodiments, the normal operating data of each subsystem in the electromechanical control system is obtained based on the following method:

[0020] Perform polynomial fitting on the historical operation data corresponding to each subsystem to obtain the fitting function corresponding to each subsystem;

[0021] The normal operation cycle of each subsystem is identified through the autocorrelation function, and the turning point is obtained through the Lagrange middle value theorem;

[0022] By marking the turning abnormal points, abnormal data in each subsystem is removed to obtain normal operating data of each subsystem in the electromechanical control system.

[0023] In some embodiments, the stability coefficient calculation module is specifically used to:

[0024] For each target subsystem, a road condition severity set consisting of road condition severity corresponding to multiple historical moments and an operation data set consisting of operation data corresponding to multiple historical moments are constructed;

[0025] Calculating the Pearson correlation coefficient between the road condition severity set and the operation data set;

[0026] Based on the Pearson correlation coefficient and the information entropy of the operation data of the target subsystem in the time window corresponding to the target time, the stability coefficient corresponding to the target subsystem is obtained.

[0027] In some embodiments, the stability coefficient calculation module is specifically used to:

[0028] The Pearson correlation coefficient between the road condition severity set and the operation data set is calculated according to the standard deviation and covariance of the road condition severity set and the operation data set.

[0029] In some embodiments, the stability coefficient calculation module is further specifically used for:

[0030] Performing an exponential operation on the information entropy as an exponent to obtain a third parameter, wherein a value of the third parameter is negatively correlated with the information entropy;

[0031] The stability coefficient corresponding to the target subsystem is obtained according to the ratio of the third parameter to the square of the Pearson correlation coefficient.

[0032] In some embodiments, the error degree coefficient determination module is specifically used to:

[0033] Performing exponential operation on the stability coefficient corresponding to the target subsystem as an exponent to obtain a fourth parameter, wherein the value of the fourth parameter is negatively correlated with the stability coefficient;

[0034] The product of the fourth parameter and the degree of road condition severity is used as the error degree coefficient corresponding to the target subsystem.

[0035] In some embodiments, the proportional gain component correction module is specifically used to:

[0036] Obtaining a proportional gain component in a PID controller for regulating electrical power;

[0037] A correction coefficient is determined based on the error degree coefficient, and a target proportional gain component is obtained according to a product of the correction coefficient and the proportional gain component.

[0038] In some embodiments, the subsystems in the electromechanical control system include one or more of a traffic signal control system, a road lighting system, an electronic toll collection system, a monitoring and security system.

[0039] The present invention has the following beneficial effects:

[0040] (1) In the highway electromechanical control system based on big data cloud service provided in some embodiments of this specification, the severity of the road condition is determined by combining the unit time traffic volume of the highway and the actual operation data and expected operation data of each subsystem at the target time, and then the error degree coefficient and target proportional gain component corresponding to each subsystem are determined by combining the stability coefficient of each subsystem itself, and the power consumption of the corresponding subsystem is adjusted based on the target proportional gain component. It can realize accurate adaptive adjustment and correction of the errors generated by the highway electromechanical control system in the harsh environment when integrating and controlling various system modules, optimize the power supply logic of the integrated system to each subsystem, thereby improving the operation efficiency, safety and user experience of the highway;

[0041] (2) In the highway electromechanical control system based on big data cloud service provided in some embodiments of this specification, the stability coefficient corresponding to each subsystem is calculated by combining the degree of correlation between the road condition severity set and the operation data set of each subsystem at multiple historical moments and the degree of disorder of the operation data of each subsystem in the time period corresponding to the target moment. The stability coefficient of each subsystem can be measured by comprehensively considering both the influence of road conditions and the internal stability of the subsystem, so as to more accurately obtain the error degree coefficient corresponding to each subsystem in the subsequent process. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 An exemplary module diagram of a highway electromechanical control system based on big data cloud service provided by one embodiment of the present invention;

[0044] Figure 2An exemplary flow chart of a highway electromechanical control method based on big data cloud service provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, features and effects of a highway electromechanical control system based on big data cloud service proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0047] The specific scheme of a highway electromechanical control system based on big data cloud service provided by the present invention is described in detail below with reference to the accompanying drawings.

[0048] See also Figure 1 , which shows an exemplary module diagram 100 of a highway electromechanical control system based on big data cloud service provided by an embodiment of the present invention. In some embodiments, the system may include a road condition severity determination module 110, a stability coefficient calculation module 120, an error degree coefficient determination module 130, a proportional gain component correction module 140, and an electric power adjustment module 150. Among them:

[0049] The road condition severity determination module 110 can be used to determine the severity of the road condition based on the unit time traffic volume of the highway at the target moment, and the actual operation data and expected operation data of each subsystem in the highway electromechanical control system at the target moment.

[0050] The stability coefficient calculation module 120 can be used to calculate the stability coefficient corresponding to each subsystem based on the degree of correlation between the road condition severity set and the operation data set of each subsystem in the highway electromechanical control system at multiple historical moments, and the degree of disorder of the operation data of each subsystem in the time period corresponding to the target moment.

[0051] The error degree coefficient determination module 130 can be used to correct the stability coefficient corresponding to each subsystem according to the severity of the road condition, and obtain the error degree coefficient corresponding to each subsystem in the highway electromechanical control system.

[0052] The proportional gain component correction module 140 can be used to correct the proportional gain component in the PID controller used for electric power regulation according to the error degree coefficient to obtain a target proportional gain component corresponding to each subsystem.

[0053] The power consumption adjustment module 150 can be used to adjust the power consumption of the corresponding subsystems in the highway electromechanical control system according to the target proportional gain component to compensate for the power consumption error of each subsystem.

[0054] For more details about the above modules, please refer to other places in this manual (for example Figure 2 part and its related description), which will not be described in detail here.

[0055] It should be understood that Figure 1 The highway electromechanical control system and its modules based on big data cloud service shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by those skilled in the art that the above-mentioned system can be implemented using computer executable instructions and / or included in a processor control code, for example, such codes are provided on a carrier medium such as a disk, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of this specification can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0056] See also Figure 2 , which shows an exemplary flowchart 200 of a highway electromechanical control method based on big data cloud service provided by an embodiment of the present invention. It should be noted that each step in the flowchart 200 can be executed by each module in the highway electromechanical control system based on big data cloud service, so as to adjust the power consumption of each subsystem in the highway electromechanical control system and compensate for the power consumption error of each subsystem, thereby improving the accuracy of resource allocation during system adjustment.

[0057] In the highway electromechanical control system, the integrated system analyzes data through the PID (proportional-integral-differential) controller to obtain real-time errors, and then regulates the power consumption of each subsystem power supply to ensure the stability of the overall system. However, external environmental factors (such as temperature changes affecting the performance of electronic components, etc.) will have different degrees of impact on each subsystem in the highway electromechanical control system, resulting in deviations between the actual power consumption of each subsystem and the expected value. This error will cause the corresponding subsystem to have an unstable operating state, and even affect the normal operation of the entire system.

[0058] Therefore, in order to ensure the stability and reliability of the entire system, the power input of each subsystem needs to be accurately monitored and controlled to adapt to the power demand fluctuations caused by changes in the external environment, thereby ensuring the stable operation of each subsystem.

[0059] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0060] S210, determining the severity of the road condition according to the unit time traffic volume of the highway at the target time, and the actual operation data and expected operation data of each subsystem in the highway electromechanical control system at the target time. In some embodiments, S210 can be performed by the above-mentioned road condition severity determination module 110.

[0061] In the embodiment of the present invention, it is considered that when the electromechanical control system uses a PID controller to adjust the power consumption of each subsystem, changes in the external environment will affect the operating efficiency of each subsystem, and different subsystems have different sensitivities to power consumption. Therefore, in order to improve the stability of the overall system, ensure user experience and safe operation of the highway, it is necessary to design an adaptive power resource allocation control strategy to optimize the electromechanical system of the highway. Among them, the subsystem may include one or more of a traffic signal control system, a road lighting system, an electronic toll collection system, and a monitoring and safety system.

[0062] It is understandable that, under normal circumstances, bad highway conditions will cause a large error between the actual operating data and the expected operating data of each subsystem in the electromechanical control system. For example, in a foggy environment, the road lighting system will cause a difference between its actual lighting brightness and the expected lighting brightness. At this time, the road lighting system needs to dynamically adjust the power to increase the brightness to ensure the visibility of the highway. In the subsequent analysis, for road conditions with low visibility, the road lighting system needs to set a larger power control amount. At the same time, bad road conditions (such as heavy rain, heavy snow, strong wind, etc.) usually reduce the driver's willingness to travel, resulting in a decrease in traffic volume on the highway. When the traffic volume on the target highway decreases, the traffic volume reduction factor can be corrected accordingly through the operating status of its electromechanical control system, so as to obtain the final degree of bad road conditions on the target highway.

[0063] Based on the above analysis, in an embodiment of the present invention, an integrated sensor can be used to collect the real-time power consumption of each subsystem (including traffic signal control system, road lighting system, electronic toll collection system, monitoring and safety system, etc.) in the electromechanical control system of the target highway section at each moment. The integrated sensor is a multifunctional device that can be used to obtain data of various physical quantities, such as temperature, humidity, light brightness, etc. In some embodiments, the integrated sensor can monitor the current and voltage of each subsystem, thereby obtaining the power consumption of each subsystem based on the voltage and current.

[0064] In some embodiments, real-time operation data of each subsystem can also be collected, such as the brightness of the road lighting system, the clarity of the monitoring and safety system, the response time of the electronic toll collection system, and the timing cycle of the traffic signal control system. At the same time, the number of vehicles passing through the target highway section per unit time in the time period corresponding to each moment can also be collected through vehicle flow detection.

[0065] In an embodiment of the present invention, the historical power consumption and historical operation data of each subsystem in the electromechanical control system of the target highway section under different weather conditions in the past month can be collected to build a historical database of the electromechanical control system corresponding to the target highway section. In some embodiments, the historical database can be stored on the cloud server, and the required data can be obtained from the cloud server through the network during subsequent processing.

[0066] Furthermore, the road condition severity determination module 110 can obtain the unit time vehicle flow and actual operation data (i.e., the implemented operation data corresponding to the current moment or the specified moment) collected at the target moment (e.g., the current moment or any specified moment), and determine the expected operation data corresponding to the target moment based on the electromechanical control system historical database, and then determine the severity of the road condition corresponding to the target moment in combination with the unit time vehicle flow, actual operation data and expected operation data corresponding to the target moment.

[0067] In some embodiments, the calculation process of the road condition severity can be expressed as follows:

[0068]

[0069] in, Indicates the unit time traffic volume of the expressway at the target time (the unit time traffic volume may refer to the average traffic volume within 10 minutes before the target time); Represents the total number of subsystems of the electromechanical control system of the target highway; Represents the target highway electromechanical control system subsystems; Indicates The actual operation data of each subsystem at the target time (i.e. the real-time operation data corresponding to the target time); Indicates Expected operating data corresponding to each subsystem; Represents the severity of the highway road conditions at the target time.

[0070] In the embodiment of the present invention, the aforementioned expected operation data can be obtained based on the following method:

[0071] First, a time series analysis is performed on the historical operating data corresponding to each subsystem to identify abnormal data, and the normal operating data of each subsystem in the electromechanical control system is determined based on the abnormal data; then, the normal operating data corresponding to each subsystem is obtained, and the average value of the normal operating data is calculated to obtain the expected operating data corresponding to each subsystem.

[0072] Specifically, in some embodiments of the present invention, the collected historical database can be screened to filter out data under normal weather conditions. For example, in some embodiments, a time series analysis can be performed on the historical data in the historical database, and the historical data of each subsystem can be polynomially fitted to obtain the fitting function of its historical data. The typical normal operating cycle of each subsystem can then be identified through the autocorrelation function, where the abnormal weather operating data will appear as anomalies or surges in the time series. In some embodiments, the turning point anomalies in the historical data can be obtained through the Lagrange mean value theorem, and then the abnormal data can be removed by marking the anomalies to obtain the normal operating data of each subsystem in the electromechanical control system. Next, for each subsystem, the average value of its historical data under normal weather conditions can be calculated, and then the average value can be used as the expected operating data of the corresponding subsystem. It should be noted that the expected operating data It can represent the typical operating level of each subsystem without abnormal weather influence. It can be used for subsequent analysis and adjustment of electromechanical control systems.

[0073] It should also be pointed out that in the embodiments of the present invention, Indicates that The difference between the actual operating data of each subsystem at the target time and the expected operating data is calculated. It means that the difference between the actual operating data and the expected operating data of all subsystems of the target highway electromechanical control system at the target time is averaged. The larger the average value is, the more the actual operating data of the target highway electromechanical control system at the target time deviates from the expected operating data. Therefore, the road condition will be more severe, and more control or greater adjustment force will be required to maintain the expected operating state.

[0074] It represents the product of the exponential function value corresponding to the unit time traffic volume of the highway at the target moment (the exponential function with the natural constant e as the base is used in the embodiment of the present invention) and the average difference between the operating data and the expected operating data of all subsystems in the overall system at the target moment. The larger the value of the product, the greater the deviation between the actual operating data and the expected operating data of each subsystem in the system when the traffic volume of the target highway is small, which means that the operating state of the overall system is far from the expectation, which may lead to poor road conditions or affect traffic fluidity, that is, the road condition is more severe at this time. The larger the value of .

[0075] In short, in the embodiment of the present invention, for each subsystem, the road condition severity determination module 110 can calculate the average value of the difference between the actual operation data and the expected operation data of the subsystem at the target time to obtain the first parameter ( ); Then the vehicle flow per unit time corresponding to the target time is used as the exponent to perform exponential operation to obtain the second parameter (In the embodiment of the present invention, since the greater the vehicle flow per unit time, the better the road condition, the vehicle flow per unit time is negatively correlated with the value of the second parameter.) Finally, the road condition severity determination module 110 may multiply the first parameter by the second parameter to obtain the road condition severity corresponding to the target time. .

[0076] S220, according to the association degree between the road condition severity set and the operation data set of each subsystem in the highway electromechanical control system at multiple historical moments, and the disorder degree of the operation data of each subsystem in the time period corresponding to the target moment, calculate the stability coefficient corresponding to each subsystem. In some embodiments, S220 can be performed by the stability coefficient calculation module 120.

[0077] In the electromechanical control system of highways, different subsystems are affected to different degrees by the severity of road conditions due to their different functions and working principles. For example, the road lighting system and the traffic monitoring system are greatly affected under low visibility conditions such as fog and rainstorm. These systems need to provide stronger brightness and clearer monitoring images, so they need more power supply, while the electronic toll collection system and the traffic signal control system are relatively less affected because both mainly rely on electronic sensors and communication networks, rather than directly on visibility. Therefore, in some embodiments of the present invention, the stability coefficient corresponding to each subsystem can be obtained by judging the degree of correlation between the historical operating data of each subsystem in the electromechanical control system and the historical road condition severity data, and combining the degree of disorder of the operating data of each subsystem in the corresponding time period (for example, within 10 minutes before the target time) at the target time.

[0078] Specifically, in the embodiment of the present invention, for each subsystem, a model can be established using its corresponding historical data to obtain a set A of highway road condition severity at all corresponding moments in the historical database. Similarly, a set B of operating data at all corresponding moments of each subsystem in the historical database can also be obtained. Then, by calculating the standard deviation and covariance of the road condition severity set A and the operating data set B corresponding to the target subsystem (i.e., the subsystem currently being calculated), the Pearson correlation coefficient between the road condition severity set A and the operating data set B is obtained. The larger the value, the stronger the linear correlation (i.e., the degree of association) between the highway road condition severity and the operating data of the target subsystem, and the more significant the impact of the road condition severity on the operation of the target subsystem, i.e., the worse the stability of the target subsystem.

[0079] At the same time, in some embodiments, for the subsystem at the target moment, the historical one-hour time series can be regarded as a time scale window, and the operation stability factor of each subsystem at the target moment can be obtained based on the disorder degree (such as information entropy) of the system operation data within the time scale window. In some embodiments of the present invention, the two factors of road condition impact (i.e., the aforementioned road condition severity) and subsystem internal stability can be comprehensively considered. If the product of the two is larger, it means that the road condition has a greater impact on the expected operation data corresponding to each subsystem. Therefore, even when the subsystem operation has a small fluctuation, a larger adjustment amount is still required in the subsequent adjustment process.

[0080] Specifically, in the embodiment of the present invention, the stability coefficient corresponding to each subsystem can be calculated based on the following formula:

[0081]

[0082] in, Indicates The stability factor of each subsystem; Indicates The set of highway road condition severity at all times in the historical database corresponding to each subsystem; Indicates The operating data set of all times in the historical database corresponding to each subsystem; Indicates The road condition severity set corresponding to each subsystem and run data collection The square value of the Pearson correlation coefficient between them; Indicates The information entropy of the operating data of each subsystem in the time window corresponding to the target time (i.e., within 1 hour before the target time), Indicates The operation stability factor of each subsystem at the target time; is a fixed constant (for example, 0.01) used to prevent the denominator from being zero.

[0083] In summary, in this manual, Can indicate the The road condition severity set corresponding to each subsystem and run data collection The ratio of the square of the Pearson correlation coefficient between the target time and the operation stability factor obtained based on the information entropy of the operation data in the time window corresponding to the target time, that is, The degree of disorder of the current operating data is corrected by the degree of influence of the bad road conditions in the historical data corresponding to each subsystem on its operating data, and the degree of influence is compared with its operating stability factor. If the operating stability factor is large (i.e., the operating data fluctuates less and the subsystem is more stable), but the degree of influence is large (i.e., the degree of correlation is high, indicating that the bad road conditions have a greater impact on the subsystem operating data), their ratio will become smaller. The smaller the value, the The operating stability coefficient of each subsystem at the current moment is small. The smaller the value, the greater the amount of adjustment required when the subsequent electromechanical system adjusts the power consumption of the subsystem to ensure the stable operation of the overall system.

[0084] Specifically, in some embodiments of the present invention, the stability coefficient calculation module 120 can obtain the target subsystem (for example, The information entropy of the operating data of the subsystem) in the time window corresponding to the target time , and then the information entropy As an exponent, an exponential operation is performed (in the embodiment of the present invention, an exponential function with the natural constant e as the base is used), and the third parameter ( ), where the value of the third parameter is related to the information entropy Further, the stability coefficient calculation module 120 can calculate the stability coefficient according to the square of the third parameter and the aforementioned Pearson correlation coefficient ( ) to obtain the stability coefficient corresponding to the target subsystem .

[0085] S230, modifying the stability coefficient corresponding to each subsystem according to the severity of the road condition to obtain the error degree coefficient corresponding to each subsystem in the highway electromechanical control system. In some embodiments, S230 can be executed by the error degree coefficient determination module 130.

[0086] By calculating through the above steps, the severity of the road condition corresponding to the target time and the stability coefficient of each subsystem corresponding to the target time can be obtained.

[0087] Furthermore, the error degree coefficient determination module 130 can correct the stability coefficient corresponding to each subsystem according to the severity of the road condition to obtain the error degree coefficient corresponding to each subsystem in the highway electromechanical control system. In some embodiments, the calculation process of the error degree coefficient can be expressed as follows:

[0088]

[0089] in, Indicates the severity of the highway road conditions at the target time; Indicates the target highway electromechanical control system The stability factor of each subsystem; Indicates the target highway electromechanical control system The power consumption error coefficient of each subsystem. Represents the severity of highway road conditions at the target time and the The product of the exponential function values ​​calculated by the stability coefficients of the subsystems is the product of the exponential function values ​​calculated by the severity of the highway road conditions at the target time. The stability coefficient corresponding to each subsystem is corrected. The larger the value, the worse the road condition of the highway is at the moment, and / or the stability coefficient of the subsystem in the highway electromechanical control system is. The stability coefficient of the subsystem is small, which means that If each subsystem is greatly affected by external interference factors and its own system is unstable, the system will produce more power errors, that is, The larger the value of , the larger the adjustment amount needs to be set in the subsequent analysis to ensure the normal operation of the overall system.

[0090] In other words, in the embodiment of the present invention, the error degree coefficient determination module 130 can determine the stability coefficient corresponding to the target subsystem. As an exponent, an exponential operation is performed (in the embodiment of the present invention, an exponential function with the natural constant e as the base is used), and the fourth parameter ( ), where the value of the fourth parameter is related to the stability coefficient Furthermore, the error degree coefficient determination module 130 can compare the fourth parameter with the above-mentioned road condition severity The product of ) as the error degree coefficient corresponding to the target subsystem .

[0091] S240, correcting the proportional gain component in the PID controller for power consumption regulation according to the error degree coefficient to obtain a target proportional gain component corresponding to each subsystem. In some embodiments, S240 may be performed by the proportional gain component correction module 140.

[0092] By calculating each subsystem separately through the above method, the error degree coefficient corresponding to each subsystem can be obtained. Further, the proportional gain component correction module 140 can correct the proportional gain component in the PID controller used for power regulation according to the error degree coefficient to obtain the target proportional gain component corresponding to each subsystem.

[0093] Specifically, in the embodiment of the present invention, the calculation process of the target proportional gain component can be expressed as follows:

[0094]

[0095] in, Indicates the electromechanical control system The error degree coefficient of each subsystem at the target time; represents the proportional gain component of the output before regulation, which is the proportional regulation coefficient in the PID controller used for regulating electric power, and can be calculated by the PID controller; It represents the target proportional gain component output after correction using the error degree coefficient.

[0096] It can be understood that when the error degree coefficient is larger, it means that the target subsystem in the highway electromechanical control system has obvious power consumption error during operation. Therefore, for the PID controller used for power regulation in the system, its proportional gain coefficient can be corrected , so that the controller used for power regulation can stably output the control quantity under the operating state affected by this error, so as to ensure the stable operation of the highway electromechanical control system.

[0097] In other words, in the embodiment of the present invention, the proportional gain component correction module 140 can obtain the proportional gain component in the PID controller used for power regulation based on , and then determine the correction factor based on the error degree coefficient ( ), and obtain the target proportional gain component according to the product of the correction coefficient and the proportional gain component .

[0098] S250, adjusting the power consumption of the corresponding subsystems in the highway electromechanical control system according to the target proportional gain component to compensate for the power consumption error of each subsystem. In some embodiments, S250 can be executed by the power consumption adjustment module 150.

[0099] It can be understood that by calculating each subsystem separately through the above steps, the target proportional gain component corresponding to each subsystem can be obtained. Further, the electric power adjustment module 150 can adjust the electric power consumption of the corresponding subsystem in the highway electromechanical control system according to the target proportional gain component to compensate for the electric power consumption error of each subsystem, realize the precise control and stability optimization of the highway electromechanical control system, and improve the safety and user experience of the highway.

[0100] It should be noted that the proportional gain component is an important parameter in the PID control loop, which can be used to adjust the output of the controller in response to changes in the process variable. In the embodiment of the present invention, the target proportional gain component can be understood as the corrected proportional gain component, and its subsequent adjustment process can refer to the PID control principle, which will not be discussed in detail in this specification.

[0101] In some embodiments, the target proportional gain component It is also possible to combine the integral gain component ( ) and / or the derivative gain component ( ) to perform PID calculations together, so as to accurately adjust the power consumption of each subsystem in the highway electromechanical control system and optimize the power consumption control effect of each subsystem.

[0102] In summary, the beneficial effects that may be brought about by the present invention include but are not limited to: (1) In the highway electromechanical control system based on big data cloud service provided in some embodiments of this specification, the severity of the road condition is determined by combining the unit time traffic volume of the highway and the actual operation data and expected operation data of each subsystem at the target time, and then the error degree coefficient and target proportional gain component corresponding to each subsystem are determined in combination with the stability coefficient of each subsystem itself, and the power consumption of the corresponding subsystem is adjusted based on the target proportional gain component, so that accurate adaptive adjustment of the errors generated by the highway electromechanical control system in harsh environments when integrating and controlling various system modules can be achieved. Correct and optimize the power supply logic of the integrated system for each subsystem, thereby improving the operating efficiency, safety and user experience of the highway; (2) In the highway electromechanical control system based on big data cloud service provided in some embodiments of this specification, by combining the correlation between the road condition severity set and the operating data set of each subsystem at multiple historical moments and the disorder degree of the operating data of each subsystem in the time period corresponding to the target moment, the stability coefficient corresponding to each subsystem can be calculated. The two factors of road condition impact and subsystem internal stability can be comprehensively considered to measure the stability coefficient of each subsystem, so as to more accurately obtain the error degree coefficient corresponding to each subsystem in the subsequent process.

[0103] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or may be any other beneficial effects that may be obtained.

[0104] It should also be noted that the order of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0106] The basic concepts of the present invention have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the scope of the exemplary embodiments of this specification.

Claims

1. A highway electromechanical control system based on big data cloud service, characterized in that: The system comprises: A road condition severity determination module is used to determine the severity of the road condition based on the unit time traffic volume of the expressway at a target time, and the actual operation data and expected operation data of each subsystem in the expressway electromechanical control system at the target time; A stability coefficient calculation module, used to calculate the stability coefficient corresponding to each subsystem in the highway electromechanical control system according to the degree of correlation between the road condition severity set and the operation data set of each subsystem at multiple historical moments, and the disorder degree of the operation data of each subsystem in the time period corresponding to the target moment; An error degree coefficient determination module is used to correct the stability coefficient corresponding to each subsystem according to the severity of the road condition, and obtain the error degree coefficient corresponding to each subsystem in the highway electromechanical control system; A proportional gain component correction module, used to correct the proportional gain component in the PID controller used for electric power regulation according to the error degree coefficient, and obtain a target proportional gain component corresponding to each subsystem; An electric power adjustment module, used to adjust the electric power of the corresponding subsystem in the highway electromechanical control system according to the target proportional gain component to compensate for the electric power error of each subsystem; The calculation process of the road condition severity can be expressed as follows: in, Indicates the traffic volume per unit time of the expressway at the target time; Represents the total number of subsystems of the electromechanical control system of the target highway; Represents the target highway electromechanical control system subsystems; Indicates The actual operation data of each subsystem at the target time; Indicates Expected operating data corresponding to each subsystem; Represents the severity of the highway road conditions at the target time; The stability coefficient calculation module is specifically used for: For each target subsystem, a road condition severity set consisting of road condition severity corresponding to multiple historical moments and an operation data set consisting of operation data corresponding to multiple historical moments are constructed; Calculating the Pearson correlation coefficient between the road condition severity set and the operation data set; Based on the Pearson correlation coefficient and the information entropy of the operation data of the target subsystem in the time window corresponding to the target time, a stability coefficient corresponding to the target subsystem is obtained; The stability coefficient corresponding to each subsystem can be calculated based on the following formula: in, Indicates The stability factor of each subsystem; Indicates The set of highway road condition severity at all times in the historical database corresponding to each subsystem; Indicates The operating data set of all times in the historical database corresponding to each subsystem; Indicates The road condition severity set corresponding to each subsystem and run data collection The square value of the Pearson correlation coefficient between them; Indicates The information entropy of the operating data of each subsystem in the time window corresponding to the target time, Indicates The operation stability factor of each subsystem at the target time; is a fixed constant; The calculation process of the error degree coefficient can be expressed as follows: in, Indicates the severity of the highway road conditions at the target time; Indicates the target highway electromechanical control system The stability factor of each subsystem; Indicates the target highway electromechanical control system The error degree coefficient of each subsystem; The calculation process of the target proportional gain component can be expressed as follows: in, Indicates the electromechanical control system The error degree coefficient of each subsystem at the target time; Represents the proportional gain component of the output before regulation, which is the proportional regulation coefficient in the PID controller used for regulating electric power; It represents the target proportional gain component output after correction using the error degree coefficient.

2. The highway electromechanical control system based on big data cloud service as claimed in claim 1, characterized in that: The expected operating data is obtained based on the following method: Performing time series analysis on the historical operation data corresponding to each subsystem to identify abnormal data, and determining the normal operation data of each subsystem in the electromechanical control system based on the abnormal data; The normal operating data corresponding to each subsystem is obtained, and the average value of the normal operating data is calculated to obtain the expected operating data corresponding to each subsystem.

3. The highway electromechanical control system based on big data cloud service as claimed in claim 2, characterized in that: The normal operating data of each subsystem in the electromechanical control system is obtained based on the following method: Perform polynomial fitting on the historical operation data corresponding to each subsystem to obtain the fitting function corresponding to each subsystem; The normal operation cycle of each subsystem is identified through the autocorrelation function, and the turning point is obtained through the Lagrange middle value theorem; By marking the turning abnormal points, abnormal data in each subsystem is removed to obtain normal operating data of each subsystem in the electromechanical control system.

4. The highway electromechanical control system based on big data cloud service as claimed in claim 1, characterized in that: The stability coefficient calculation module is specifically used for: The Pearson correlation coefficient between the road condition severity set and the operation data set is calculated according to the standard deviation and covariance of the road condition severity set and the operation data set.

5. The highway electromechanical control system based on big data cloud service according to any one of claims 1 to 4, characterized in that: The subsystems in the electromechanical control system include one or more of a traffic signal control system, a road lighting system, an electronic toll collection system, a monitoring and safety system.

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