A fieldbus-based intelligent building control method and system

Abstract

The present invention discloses an intelligent building control system based on field bus, including a collection module, a positioning module, a communication module and a processing module. The collection module collects building environment to generate environmental values, and the environmental values ​​are wirelessly sent to the processing module through the communication module. After the processing module fuses and compresses the environmental values, the environmental values ​​are compared with reference values, and corresponding instructions are issued according to the comparison results. The positioning module locates the building area where the collection module is located according to the environmental values. The present invention generates early warning information according to instructions one and two, and sends it to the user end for early warning. The control system collects real-time values ​​of multiple states of the internal environment of the building. When the collected values ​​change abnormally, control is made in time, and a warning signal is generated according to the abnormal values ​​and sent to the user end to prompt the residents. The intelligent degree is high and the safety is good.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent control, and in particular to an intelligent building control system based on a field bus. Background Art

[0002] The construction of intelligent buildings is a good embodiment of the development of smart cities. The real estate industry has regarded intelligent buildings as a strategic goal for sustainable development. The application of intelligent technology in buildings can effectively improve people's living environment, energy-saving society construction and promote the sustainable development of the construction industry. The building control system is a system for centralized management and monitoring of various electromechanical equipment in the building, including air-conditioning fresh air units, supply and exhaust fans, sumps and drainage pumps, elevators, power distribution, lighting, etc.

[0003] The Chinese patent application number 201410626522.2 discloses a fieldbus-based intelligent building control system, including: an underlying sensor, an actuator, and a subsystem intelligent gateway, wherein the actuator is electrically connected to the output end of the underlying sensor, and the output end of the actuator is electrically connected to the subsystem intelligent gateway via an RS485 bus, and the subsystem intelligent gateway is electrically connected to a central server and the values ​​of the subsystem intelligent gateway are backed up to the central server, and the central server is electrically connected to a cloud host and the values ​​are backed up to the cloud host. Through the above-mentioned method, the fieldbus-based intelligent building control system of the present invention has the advantages of collaborative work of various intelligent gateway terminals, optimized design, novel structure, stable performance, reduced cost, improved efficiency, etc., and has a broad market prospect in the popularization of fieldbus-based intelligent building control systems.

[0004] The above technology has the following shortcomings: the existing building control system only collects environmental signals inside the building. When the environmental values ​​change abnormally, the control system generates an abnormal signal based on the abnormal value and sends the abnormal signal to the central server. The central server issues an alarm to prompt the staff, and then the staff conducts patrol inspections on the areas where the abnormal values ​​are sent. The staff inspection requires a lot of manpower, and the inspection efficiency is low, which leads to poor security of the building control system. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent building control system based on field bus to solve the shortcomings of the background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a fieldbus-based intelligent building control method, the control method comprising the following steps:

[0007] S1: The collection end collects building environment in real time, generates environmental values, and sends the environmental values ​​to the server based on wireless communication;

[0008] S2: The server processes the environment value and compares it with the reference value;

[0009] S3: The environment value is greater than the reference value, and the server generates a command and sends it to the control end;

[0010] S4: The environment value is less than the reference value, and the server generates instruction 2 and sends it to the control end.

[0011] S5: Generate warning information according to instruction 1 and instruction 2, and send it to the user end for warning.

[0012] In a preferred embodiment, the environmental values ​​include smoke concentration values, air quality values, environmental temperature and humidity values, and environmental light values, and the reference values ​​include smoke concentration thresholds, air quality thresholds, environmental temperature and humidity thresholds, and environmental light thresholds.

[0013] In a preferred embodiment, in step S3, the smoke concentration value is greater than the smoke concentration threshold, the air quality value is greater than the air quality threshold, the ambient temperature and humidity value is greater than the ambient temperature and humidity threshold, and the ambient light value is greater than the ambient light threshold; in step S4, the smoke concentration value is less than the smoke concentration threshold, the air quality value is less than the air quality threshold, the ambient temperature and humidity value is less than the ambient temperature and humidity threshold, and the ambient light value is greater than the ambient light threshold.

[0014] In a preferred embodiment, the user terminal includes a mobile phone App, warning information is prompted through the mobile phone App, and the user views the internal environment information of the building through the mobile phone App. The administrator uses the user terminal to effectively check whether the user's authority is legal.

[0015] The present invention also provides a fieldbus-based intelligent building control system, the control system is used to implement a fieldbus-based intelligent building control method, including a collection module, a positioning module, a communication module and a processing module;

[0016] The acquisition module collects the building environment to generate environmental values, which are wirelessly sent to the processing module through the communication module. After the processing module integrates and compresses the environmental values, it compares the environmental values ​​with the reference values ​​and issues corresponding instructions based on the comparison results. The positioning module locates the building area where the acquisition module is located based on the environmental values.

[0017] In a preferred embodiment, the acquisition module includes a smoke sensor, an air quality sensor, a temperature and humidity sensor, and a light sensor. The smoke sensor is used to detect the ambient smoke concentration, the air quality sensor is used to detect the air quality, the temperature and humidity sensor is used to detect the ambient temperature and humidity, and the light sensor is used to detect the ambient brightness.

[0018] In a preferred embodiment, the processing module compares the environmental value with the reference value based on the PID, and issues corresponding instructions according to the comparison result. The calculation formula is:

[0019] e(t)=r(t)-y(t)

[0020] Among them, r(t) is the input set by the system, y(t) is the actual output value of the system, and e(t) represents the deviation between y(t) and r(t).

[0021] In a preferred embodiment, the processing module performs numerical fusion based on a BP neural network, comprising the following steps:

[0022] According to the DCHP clustering routing protocol, the sensor network is clustered and the first cluster head and the second cluster head are selected;

[0023] The first cluster head sends its own information and the information of the nodes in the cluster to the sink node, and the sink node constructs a BP neural network based on the information sent;

[0024] Using the member nodes in the cluster and the first cluster head to perform fusion operations;

[0025] The sensor nodes of each cluster process the collected numerical information and send the results to the first cluster head node for further fusion processing according to the neuron functions of the hidden layer and the output layer;

[0026] The first cluster head sends the fused feature value to the adjacent second cluster head, which then sends it to the sink node via multi-hop routing.

[0027] In a preferred embodiment, the processing module compresses the numerical value of the wireless sensor including the following steps:

[0028] When a non-Sink node generates a numerical stream, let n0 be the node number;

[0029] Let S<x[t1],x[t2],x[t3],...> The time series of monitoring values ​​generated by node n0;

[0030] When the first numerical point (t1, x[t1]) arrives, store it. When the second numerical point (t2, x[t2]) arrives, find a line segment so that the vertical distance from the two numerical points to this line segment is ≤ ε.

[0031] When the third value point (t3, x[t3]) arrives, check the first three value points;

[0032] The first three numerical points can be described by a line segment and meet the ε error limit requirement, waiting for the arrival of the fourth numerical point;

[0033] If the first three numerical points cannot be described by a line segment, describe the first and second numerical points with a line segment, and then search for a new line segment starting from the third numerical point;

[0034] Every time a new value point arrives, a check operation is performed until the end of the time series.

[0035] In a preferred embodiment, the positioning module comprises the following steps in locating the building area:

[0036] Get the starting hop count: The anchor node broadcasts information to other nodes, and the message contains a hop counter Hops i , the initial value is 0, count the starting hops from the anchor node, and increase the hop count by 1 for each broadcast message received. i When the anchor node information is obtained, the starting hop count is taken as the starting hop count to the anchor node;

[0037] Estimated distance: The unknown node estimates the average distance of one hop by using the starting hop number obtained in the first step and the known anchor node coordinates in the network. The calculation formula is:

[0038]

[0039] Where: (x i ,y i ),(x j ,y j ) represents the coordinates of anchor nodes i and j, h i is the starting hop number between anchor nodes i and j, and the average hop distance is represented by HopSize. Then the distance d between node o and anchor node i is o,i By d o,i =HopSize×h o,i Formula calculation.

[0040] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0041] 1. The present invention processes environmental values ​​through the server side and compares the environmental values ​​with reference values. When the environmental value is greater than the reference value, the server side generates instruction 1 and sends it to the control side. When the environmental value is less than the reference value, the server side generates instruction 2 and sends it to the control side. At the same time, early warning information is generated according to instruction 1 and instruction 2, and sent to the user side for early warning. The control system collects real-time values ​​of multiple states of the internal environment of the building. When the collected values ​​change abnormally, control is taken in time, and an early warning signal is generated according to the abnormal value and sent to the user side to prompt the residents. It has a high degree of intelligence and good safety.

[0042] 2. The present invention collects multiple parameters of the internal environment of the building, fuses and compresses the generated environmental values, and locates the area that needs to be controlled and processed based on the environmental values. While speeding up the transmission of values, it improves the positioning speed of the area and has high control efficiency.

[0043] 3. The present invention sets a fuzzy controller to calculate the output according to the change of input quantity, and uses the established fuzzy rules to continuously correct the control parameters of PID. p , K i , K d Adding it to the calculated correction value can provide better dynamic and static characteristics in the entire control process.

[0044] 4. The processing module of the present invention integrates the collected monitoring numerical information so that the final numerical information represents relevant meanings and reflects the characteristics of the monitored object in detail, thereby saving the network energy consumption of the communication module, and compressing the integrated numerical values, effectively reducing the communication overhead brought by the nodes and improving the transmission efficiency of the numerical values. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 Flow chart of the control method of the present invention.

[0047] Figure 2 It is a control system module diagram of the present invention.

[0048] Figure 3 This is the principle diagram of the fuzzy adaptive PID in the processing module of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element at the same time.

[0051] Example 1

[0052] See also Figure 1 As shown, the present embodiment provides a fieldbus-based intelligent building control method, the control method comprising the following steps:

[0053] The collection end collects building environment in real time, generates environmental values, and sends the environmental values ​​to the server end based on wireless communication;

[0054] Environmental values ​​include smoke concentration values, air quality values, ambient temperature and humidity values, and ambient light values.

[0055] The server side processes the environmental value and compares the environmental value with the reference value. When the environmental value is greater than the reference value, the server side generates instruction 1 and sends it to the control side. When the environmental value is less than the reference value, the server side generates instruction 2 and sends it to the control side. At the same time, early warning information is generated according to instruction 1 and instruction 2, and sent to the user side for early warning. The control system collects real-time values ​​of multiple states of the internal environment of the building. When the collected values ​​change abnormally, control is made in time, and early warning signals are generated according to abnormal values ​​and sent to the user side to remind residents. It has high intelligence and good security.

[0056] The reference values ​​include smoke concentration threshold, air quality threshold, ambient temperature and humidity threshold, and ambient light threshold.

[0057] The control end receives instruction one and performs control corresponding to instruction one, and the control end receives instruction two and performs control corresponding to instruction two;

[0058] When the environmental value is greater than the reference value, it means that the smoke concentration value is greater than the smoke concentration threshold, the air quality value is greater than the air quality threshold, the environmental temperature and humidity value is greater than the environmental temperature and humidity threshold, and the environmental light value is greater than the environmental light threshold. Among them, if the air quality value is greater than the air quality threshold, it means that the air quality is good.

[0059] Therefore, instruction 1 only sends instructions to the control end based on the smoke concentration, ambient temperature and humidity, and ambient light. If the smoke concentration is too high, it means that there may be a fire inside the building. At this time, the control end controls the sprinkler system to spray and extinguish the fire in the collection area. If the ambient temperature and humidity are too high, it means that the building is hot and humid. The control end controls the fresh air system to dehumidify and cool the collection area. If the ambient light value is too high, it means that the building is too bright. At this time, the control end controls the lighting equipment to reduce the brightness inside the building (too bright inside the building will cause damage to the eyes).

[0060] When the environmental value is less than the reference value, it means that the smoke concentration value is less than the smoke concentration threshold, the air quality value is less than the air quality threshold, the ambient temperature and humidity value is less than the ambient temperature and humidity threshold, and the ambient light value is greater than the ambient light threshold. Among them, the smoke concentration value is less than the smoke concentration threshold, indicating that the interior of the building is safe and there is no fire phenomenon;

[0061] Therefore, instruction 2 only sends instructions to the control end based on the air quality value, ambient temperature and humidity value, and ambient light value. If the air quality value is too low, it means that the air inside the building may contain toxic gases. The control end controls the disinfection and exhaust system to disinfect and exhaust the collection area. If the ambient temperature and humidity value is too low, it means that the inside of the building is dry and cold. The control end controls the fresh air system to humidify and heat the collection area. If the ambient light value is too small, it means that the inside of the building is too dark. The control end controls the lighting equipment to increase the brightness inside the building.

[0062] The user terminal includes a mobile phone App, and warning information is prompted through the mobile phone App. Users can view the internal environment information of the building through the mobile phone App. The administrator uses the user terminal to effectively check whether the user's authority is legal. Therefore, if the user wants to log in to the system, he must verify his identity through the registration interface.

[0063] The administrator enters the user login interface through the Web interface, enters the user name and the corresponding password, and then receives a 6-digit digital verification code. After entering it, management can be performed. The user completes the user login through the mobile app. For this, the user should have the corresponding user name and password, and enter the graphic verification code. The user end will then MD5 encrypt the user name and password and encapsulate them. The user end will then send the encapsulated file to the network server through the network. The network server matches the user information in the numerical library through ADO.NET (ActiveXDataObjects, ADO). If the user name and password entered by the user on the login interface have been entered into the numerical library server, and there is no error in the input of the randomly generated verification code, then the user can pass the verification. After verification, the user can obtain the corresponding authority and can view the environmental information inside the building.

[0064] Example 2

[0065] See also Figure 2 As shown, the field bus-based intelligent building control system described in this embodiment includes a collection module, a positioning module, a communication module and a processing module;

[0066] in,

[0067] Collection module: used to collect building environment and generate environmental values, including smoke concentration value, air quality value, ambient temperature and humidity value and ambient light value.

[0068] Communication module: used to send environmental values ​​to the processing module based on wireless communication;

[0069] The communication module is an ESP8266 wireless transmission unit. The ESP8266 wireless transmission unit is mainly responsible for information interaction, receiving control instructions or sending feedback information. The ESP8266 wireless transmission unit adopts internal integrated power management and system power consumption design, and can switch between sleep mode and working mode to reduce the non-essential power consumption of the system. When ESP8266 is in sleep mode, the current consumption is less than 12uA, and only the RTC is turned on for calibration and watchdog to prevent the system from freezing. When in wifi connection state, the power consumption is less than 1mA. In order to further reduce power consumption and meet the needs of wearable devices, the internal integrated power amplifier can be programmed to reduce the output power to adapt to short-range communication. The wifi-based communication equipment achieves the purpose of network connection and numerical communication.

[0070] Processing module: After fusing and compressing the environmental values, the environmental values ​​are compared with the reference values, and corresponding instructions are issued according to the comparison results. The reference values ​​include the smoke concentration threshold, air quality threshold, ambient temperature and humidity threshold, and ambient light threshold.

[0071] Positioning module: Based on the environmental value positioning acquisition module, the control system collects multiple parameters of the internal environment of the building, fuses and compresses the generated environmental values, and locates the area that needs to be controlled based on the environmental values. While speeding up the transmission of values, it also improves the positioning speed of the area and has high control efficiency.

[0072] The acquisition module includes a smoke sensor, an air quality sensor, a temperature and humidity sensor, and a light sensor. The smoke sensor is used to detect the ambient smoke concentration, the air quality sensor is used to detect the air quality, the temperature and humidity sensor is used to detect the ambient temperature and humidity, and the light sensor is used to detect the ambient brightness.

[0073] The acquisition module may also include a monitoring camera for monitoring the lighting equipment inside the building to ensure normal operation. After entering the monitoring interface, the relevant management personnel can view the lighting equipment in the entire building, find the fault location, and quickly repair it.

[0074] When a lighting failure occurs in a certain place, there will be a push display on the lighting control monitoring interface, which is convenient for staff to rush to the scene for repairs more quickly. From a functional point of view, the monitoring camera monitors the entire lighting equipment very well, and timely feedback on faults can make the management of lighting equipment more convenient and quick.

[0075] The processing module adopts B / S architecture and MVC design mode, including three parts: front-end page, back-end service program and numerical library. The front-end page is implemented using JSP technology, the back-end service program is developed using Java, and the numerical library part uses MySQL. The processing module as a whole uses the integrated architecture of Structs+Spring+Mybatis, builds a wireless LAN through the ESP8266 wireless transmission unit, and implements the Socket communication algorithm through the back-end Java program, thereby realizing wireless communication between the processing module and the acquisition module. The processing module uses a graphical interface to achieve the purpose of wireless control and status display of the intelligent terminal module.

[0076] Example 3

[0077] The processing module compares the environmental value with the reference value and issues corresponding instructions according to the comparison result.

[0078] The processing module processes the environmental values ​​based on the PID algorithm and issues corresponding instructions for control. The PID control principle is to adjust the actuator by comparing the difference between the measured value and the given value through the proportional, integral and differential links, and then act on the controlled object. The calculation formula is:

[0079] e(t)=r(t)-y(t)

[0080] Among them, r(t) is the input set by the system, y(t) is the actual output value of the system, and e(t) represents the deviation between y(t) and r(t).

[0081] The control law expression of PID is:

[0082]

[0083] In the formula, K p , K i , K d They represent the proportional, integral and differential coefficients of PID control respectively.

[0084] The transfer function is in the form:

[0085]

[0086] When the controlled object is PID-regulated, the system gives a value r(t) and compares it with the measured value y(t). If there is a deviation, the controller adjusts it to reduce the deviation. In PID regulation, P is the proportional link, which can effectively improve the response speed of the system. However, when K p When it is too large, it will cause the system to oscillate; I is the integral link, which can effectively eliminate the static error in the system, but when K i When K is too large, the adjustment accuracy will be insufficient and the static error cannot be completely eliminated. D is the differential link, which can improve the dynamic performance of the system and reduce the overshoot. However, when K p If it is too large, it will increase the adjustment time.

[0087] See also Figure 3 As shown, this embodiment proposes fuzzy adaptive PID control, K p , K i , K d It can make adjustments as the working conditions change and does not require the establishment of an accurate mathematical model. It can adapt to the controlled objects that change over time. The use of fuzzy adaptive PID has a better control effect than ordinary PID when controlling controlled objects with large delays and inertia.

[0088] The control performance of the fuzzy controller is affected by the quantization factor and the proportional factor. e Will affect the static error of the control system, K e As the static error increases, K e The larger the value, the faster the response curve changes. e When it is too large, it will cause system instability, which is mainly reflected in the increase of overshoot in the control process and the increase of adjustment time.

[0089] Quantization factor K ec Will affect the static error of the system and increase K ec Can effectively reduce overshoot, but K ec Too large a value will cause the system to react slowly and the adjustment process to take less time. ec If it is too small, the system will be more sensitive and the corresponding curve change rate will be faster, but it will cause system oscillation.

[0090] Scaling factor K u The role is similar to the proportional gain in the PID controller. uAs the response curve changes faster, the overshoot increases, causing the system to oscillate. u Too small will increase the adjustment time, K u It will not affect the steady-state error of the system, but it will affect the dynamic characteristics of the entire process control. Therefore, the selection of quantization factor and proportional factor will directly affect the control effect.

[0091] The error is quantified by:

[0092]

[0093] The quantification factor of the error change rate is:

[0094]

[0095] The scaling factor is:

[0096]

[0097] The fuzzy controller calculates the output according to the change of input quantity by using the established fuzzy rules, and constantly corrects the control parameters of PID. p , K i , K d Adding it to the calculated correction value can provide better dynamic and static characteristics in the entire control process.

[0098] Example 4

[0099] The processing module fuses and compresses the environmental values, thereby reducing the transmission amount of the values ​​and increasing the transmission speed of the values;

[0100] Numerical fusion: The collected monitoring numerical information is fused so that the final numerical information represents relevant meanings and reflects the characteristics of the monitored object in detail.

[0101] The processing module performs numerical fusion based on the BP neural network algorithm. The BP neural network algorithm is integrated into the processing module routing protocol to form a new numerical fusion model. The DCHP clustering routing protocol is combined with the BP neural network to obtain a numerical fusion algorithm DCBP based on the BP neural network. The specific steps are as follows:

[0102] (1) According to the DCHP clustering routing protocol, the entire sensor network is clustered and the first cluster head and the second cluster head are selected;

[0103] (2) The first cluster head sends information about itself and its nodes to the sink node, which constructs a BP neural network based on the information sent.

[0104] (3) Apply the idea of ​​BP algorithm to each cluster of the processing module, and use the member nodes in the cluster and the first cluster head to perform fusion operation;

[0105] (4) The sensor nodes of each cluster perform simple processing on the collected numerical information and then send the results to the first cluster head node for further fusion processing according to the neuron functions of the hidden layer and the output layer.

[0106] (5) The first cluster head sends the fused feature value to the adjacent second cluster head, which then sends it to the sink node via multi-hop routing. The selected second cluster head is only used to forward the fused numerical feature value.

[0107] In the processing module, assuming that there are n member nodes in a cluster, the number of neurons in the input layer is n, and the number of neurons in the output layer is adjusted according to actual conditions and has no direct relationship with the type of information collection and the number of member nodes.

[0108] Assuming that the number of neurons in the output layer is I, there are generally two methods to determine the number of output layer nodes: log2I when more information needs to be output, or I when less information needs to be output. The setting of the number of hidden layer neurons m is related to some factors, such as the number of training samples, the size of sample noise, etc.

[0109] The method usually used to calculate the optimal number of hidden layer neuron nodes is the trial and error method. First, a small number of hidden nodes are selected from the hidden layer for network training, and then the number of hidden nodes is slowly increased. The same sample set is used during training. The number of hidden nodes is determined when the network error is minimized. The trial and error method is currently used, and the calculation formula is: α is set to a number between 1 and 10, and the number obtained by the formula is used as the initial value of the trial and error method.

[0110] When numerical fusion is performed, relevant parameter values ​​need to be obtained through the BP neural network training process. However, since the energy resources of the nodes in the processing module are limited, after the entire network is clustered through the DCHP routing protocol, before the network can enter the stable numerical transmission process, it is necessary to use the aggregation node to complete the training before the neural network design to obtain various parameter values, such as the weights between neurons. This method can save node energy consumption and extend the network life cycle.

[0111] Note that after the network is clustered by the DCHP protocol, the first cluster head selected sends the information of the nodes in the cluster to the sink node. The process of selecting which cluster information to send to the sink node is determined according to different actual applications. Then the sink node trains the neural network based on the network information to obtain relevant parameters.

[0112] In the processing module, when a new round of cluster head election is needed, parameter transfer is required. Since the hidden layer and output layer are allocated numbers from the first cluster head, when the next round of election is carried out, the relevant parameters on these hidden layers and output layers are first retained in their own memory. After the network reaches clustering stability again, the cluster head nodes of the previous round will transmit the relevant parameters to the new first cluster head according to their current cluster, and then perform numerical fusion.

[0113] Value compression: Sensor nodes continuously monitor physical quantities such as temperature, humidity, light intensity, and carbon dioxide concentration. Even if each node records a few bytes per second, several GB of data will be generated in a month, which will bring huge communication overhead to nodes with limited energy. Therefore, a key challenge for the processing module is how to efficiently transmit these values ​​in the processing module to the sink node for subsequent processing and analysis.

[0114] An intuitive method is to let all nodes in the sensor network periodically send their monitoring values ​​to the Sink node, but this method will bring serious storage consumption and communication overhead, which is contrary to the energy efficiency required by the processing module. Because sensor nodes have only limited battery life and wireless transmission requires more energy, excessive value transmission will quickly deplete the battery and cause the node to fail.

[0115] Numerical compression in the processing module includes wavelet transform, discrete Fourier transform and discrete cosine transform, but the above three methods are not suitable for the processing module due to their high time and space complexity and high computing and storage requirements.

[0116] In this embodiment, the numerical value compression of the wireless sensor includes the following steps:

[0117] (1) Given a time series S, the maximum error bound ε;

[0118] (2) Starting from the first numerical point, greedily scan the numerical points in the sequence in order until a numerical point is scanned, so that all the numerical points before this numerical point can be approximately described by a line segment with guaranteed accuracy;

[0119] (3) After adding this numerical point, there is no line segment that can approximately describe all the numerical points that are not approximately described.

[0120] (4) Approximate all the numerical points before the last numerical point with a line segment, and then perform a greedy scan similarly starting from the last numerical point until the entire time series ends.

[0121] The specific processing logic is:

[0122] When a non-Sink node generates a value stream, let n0 be the node number. Assume S<x[t1],x[t2],x[t3],...> For the time series of monitoring values ​​generated by node n0, when the first numerical point (t1, x[t1]) arrives, it is stored. When the second numerical point (t2, x[t2]) arrives, it is easy to find a line segment so that the vertical distance from the first two numerical points to this line segment is less than or equal to ε. When the third numerical point (t3, x[t3]) arrives, we check whether the first three numerical points can be approximated by a line segment and meet the ε error limit requirement. If not, the first and second numerical points are approximated by a line segment, and then a new line segment approximation is started from the third numerical point. If so, wait for the arrival of the fourth numerical point. When the fourth numerical point arrives, similarly check whether the first four numerical points can be approximated by a line segment and meet the ε error limit requirement. In this way, every time a new numerical point arrives, we perform a similar check operation until the end of the entire time series.

[0123] By formally defining the piecewise disconnected linear approximation problem, an optimal method is provided for this problem, which not only ensures that the absolute difference between the true value and the approximate value of each numerical point does not exceed the maximum error limit ε, but also only requires O(n) time, where n represents the length of the time series. The numerical compression step is suitable for processing module applications with dense numerical streams (individual sensor nodes in the network continuously collect and send numerical values), such as environmental monitoring, health monitoring, battlefield reconnaissance and other processing module applications.

[0124] The processing module integrates the collected monitoring numerical information so that the final numerical information represents relevant meanings and reflects the characteristics of the monitored object in detail, thereby saving the network energy consumption of the communication module, and compressing the fused numerical values, effectively reducing the communication overhead brought by the nodes and improving the transmission efficiency of the numerical values.

[0125] Example 5

[0126] In the positioning process, the unknown node does not need to directly measure the distance to obtain the coordinates. It communicates with the anchor node with known coordinates in the network through digital signals to obtain its own position. The way in which the unknown node obtains its own coordinates is related to the starting hop number and the average hop distance in the network. The specific steps of positioning are as follows:

[0127] (1) Obtaining the starting hop count: The anchor node in the network broadcasts information to other nodes around it. The message is propagated hop by hop. Its format is {Id i ,x i ,y i ,Hops i}, there is a hop counter Hops in the messagei , the initial value is 0. Each node maintains an information table and counts the starting hop count from the anchor node. The starting hop count is counted by adding 1 to the hop count for each broadcast message received. i When receiving anchor node information, only the starting hop count is taken as the starting hop count to the anchor node, and the starting hop count is saved in the information table for further propagation. After the broadcast, all nodes in the network have saved the starting hop count information to each anchor node.

[0128] (2) Estimated distance: The unknown node can estimate the average distance of one hop by using the starting hop number obtained in the first step and the known coordinates of the anchor nodes in the network. The calculation formula is:

[0129]

[0130] Where: (x i ,y i ),(x j ,y j ) represents the coordinates of anchor nodes i and j, h i is the starting hop number between anchor nodes i and j, and the average hop distance is represented by HopSize. Then the distance d between node o and anchor node i is o,i By d o,i =HopSize×h o,i Formula calculation.

[0131] After the positioning module performs positioning calculation, it is necessary to perform starting hop count error analysis, average hop distance error analysis and position calculation error analysis;

[0132] in,

[0133] (1) Analysis of the error in the starting hop count: Due to the uneven distribution of nodes in the sensor network, even within the same communication range, the distances from the nodes to the communication anchor node are not equal. In the first step of obtaining the starting hop count, the positioning module counts two adjacent nodes as 1 hop regardless of the distance. When calculating the estimated distance to the anchor node, the product of the node’s hop count and the average hop distance is used. This makes the distances between all nodes and the anchor node the same within the communication radius of the anchor node. Within the communication radius of anchor node O, the starting hop counts of nodes N1, N2, N3, and N4 to node O are all 1. Therefore, the distances between the four unknown nodes and the anchor node estimated by the algorithm are equal, but the actual distances are:

[0134] d1 <d2<d3<d4

[0135] Among them, d1, d2, d3, and d4 are the distances from nodes N1, N2, N3, and N4 to node O respectively. It can be seen that directly recording the number of hops between two adjacent nodes as 1 hop will cause a large error. In order to reduce the error, the hop value should be adjusted accordingly according to the actual distance between the nodes, rather than directly recording it as 1 hop.

[0136] (2) Average hop error analysis: The average hop distance of the anchor node is calculated by the ratio of the straight-line distance between the two nodes to the starting hop number. In the actual environment, since the nodes in the network are randomly deployed and the network topology is complex and changeable, the hop path between the two nodes is often not a straight line but several broken lines, and many hops are required to communicate. Assume that the straight-line distance between the anchor nodes M and N is d. If they need to communicate through 3 hops, and the distance of each hop is d1, d2, and d3 respectively, then the actual average hop distance between M and N is h = (d1, d2, d3) / 3, while the average hop distance estimated by the positioning module is HopSize = d / 3. The estimated average hop distance is smaller than the actual hop distance. Therefore, there is a certain error when the distance from the unknown node to the anchor node is calculated using the average hop distance with error.

[0137] Secondly, when solving the distance to the anchor node, the differences of each anchor node in the network are ignored. The unknown node uses the average hop distance of the nearest anchor node to solve the distance between the two, which cannot effectively reflect the overall situation of the sensor network, and thus there are certain errors. Assume that O is an unknown node, M and N are anchor nodes, and the number of hops from O to M and N is 2. After the design calculation, the average hop distances of M and N are 10 and 15 respectively. Then, when calculating the distance from O to the two, if the average hop distance of M is used, the calculated distance is 20, and if the average hop distance of N is used, the calculated distance is 30. Even if the distances are the same but different anchor nodes are referenced, the results are different and there are certain errors. Therefore, using only the average hop distance of a single anchor node cannot well reflect the overall situation of the network. In order to reduce errors, the distribution of each node should be comprehensively considered according to the network topology.

[0138] (3) Position calculation error analysis: The positioning module uses the node coordinate calculation method to solve the position of the node to be located. Due to the accumulation of the initial hop count error and the average hop distance error, a certain position calculation error will also occur. For example, when using the trilateration method, although the calculation process is simple, if the three anchor nodes around the unknown node are collinear, the circles formed by the three anchor nodes will not intersect at one point, and the node cannot be located by this method. When the maximum likelihood estimation method is used, it can be seen from the equation AX=b that when any deviation occurs in either matrix A or matrix b, it will affect the positioning result.

[0139] Since the positioning module communicates and locates with the anchor node of known coordinates in the network through digital signals, there are errors in the number of initial hops, average hop distance, and position calculation. Therefore, this embodiment improves the errors existing in the positioning module, as follows:

[0140] (1) Initialize the network: The beacon node in the network generates a broadcast message that is propagated in a hop-by-hop manner. The message packet format is {Id i ,x i ,y i ,Hops i ,RSSI i}, Hops i The initial value is RSST. i The signal strength measured for the node, the initial value is also 0.

[0141] (2) Calculation of the first hop: After the anchor node broadcasts the information packet to the neighboring node, the starting hop count of the neighboring node is recorded as 1, and the RSSI value of the neighboring node is used as the reference value RSSI for subsequent nodes to refer to. R , then update the Hops in the information package i and RSSI i and forward the value packet in sequence.

[0142] (3) Calculate the remaining starting hop counts: The next adjacent node calculates the weighted starting hop count according to the following formula:

[0143]

[0144] In the formula, Hops i Represents the weighted starting hop count of the i-th (i>l) node after improvement, RSSI i Represents the signal strength value of the i-th node, RSSI R It means that when the communication radius of the anchor node is R, the RSSI value of the adjacent node that directly communicates with the anchor node is the RSSI reference value, and the corrected value packet is broadcast.

[0145] (4) Calculate the weighted average hop distance: After each node in the network obtains the weighted starting hop count, the weighted average hop distance calculation formula is:

[0146]

[0147] Where HopSize is the average hop distance, is the weighting coefficient, and HopSizeavg is the weighted average hop distance. Through the above steps, the initial hop count error, average hop distance error and position calculation error in the positioning module are improved, thereby reducing the positioning error, which is beneficial to improving the positioning accuracy of the positioning module.

[0148] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or value center to another website site, computer, server or value center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a server, value center or other value storage device containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0149] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0150] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0151] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0152] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0153] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0154] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0157] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0158] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A fieldbus-based intelligent building control method, characterized in that: The control method comprises the following steps: S1: The collection end collects building environment in real time, generates environmental values, and sends the environmental values ​​to the server based on wireless communication; S2: The server processes the environment value and compares it with the reference value; S3: The environment value is greater than the reference value, and the server generates a command and sends it to the control end; S4: The environment value is less than the reference value, and the server generates instruction 2 and sends it to the control end; S5: Generate warning information according to instruction 1 and instruction 2, and send it to the user end for warning; Numerical fusion based on BP neural network includes the following steps: According to the DCHP clustering routing protocol, the sensor network is clustered and the first cluster head and the second cluster head are selected; The first cluster head sends its own information and the information of the nodes in the cluster to the sink node, and the sink node constructs a BP neural network based on the information sent; Using the member nodes in the cluster and the first cluster head to perform fusion operations; The sensor nodes of each cluster process the collected numerical information and send the results to the first cluster head node for further fusion processing according to the neuron functions of the hidden layer and the output layer; The first cluster head sends the fused feature value to the adjacent second cluster head, which then sends it to the sink node via multi-hop routing; The numerical compression of wireless sensors includes the following steps: When a non-Sink node generates a numerical stream, let n0 be the node number; Let S<x[t1],x[t2],x[t3],...> The time series of monitoring values ​​generated by node n0; When the first numerical point (t1, x[t1]) arrives, store it. When the second numerical point (t2, x[t2]) arrives, find a line segment so that the vertical distance from the two numerical points to this line segment is ≤ ε. When the third value point (t3, x[t3]) arrives, check the first three value points; The first three numerical points can be described by a line segment and meet the ε error limit requirement, waiting for the arrival of the fourth numerical point; If the first three numerical points cannot be described by a line segment, describe the first and second numerical points with a line segment, and then search for a new line segment starting from the third numerical point; Whenever a new value point arrives, a check operation is performed until the end of the time series; The following steps are involved in locating the building area: Get the starting hop count: The anchor node broadcasts information to other nodes, and the message contains a hop counter Hops i , the initial value is 0, count the starting hops from the anchor node, and increase the hop count by 1 for each broadcast message received. i When the anchor node information is obtained, the starting hop count is taken as the starting hop count to the anchor node; Estimated distance: The unknown node estimates the average distance of one hop by using the starting hop number obtained in the first step and the known anchor node coordinates in the network. The calculation formula is: Where: (x i ,y i ),(x j ,y j ) represents the coordinates of anchor nodes i and j, h i is the starting hop number between anchor nodes i and j, and the average hop distance is represented by HopSize. Then the distance d between node o and anchor node i is o,i By d o,i =HopSize×h o,i Formula calculation.

2. The intelligent building control method based on field bus according to claim 1 is characterized in that: The environmental values ​​include smoke concentration values, air quality values, environmental temperature and humidity values, and environmental light values, and the reference values ​​include smoke concentration thresholds, air quality thresholds, environmental temperature and humidity thresholds, and environmental light thresholds.

3. The intelligent building control method based on field bus according to claim 2 is characterized in that: In step S3, the smoke concentration value is greater than the smoke concentration threshold, the air quality value is greater than the air quality threshold, the ambient temperature and humidity value is greater than the ambient temperature and humidity threshold, and the ambient light value is greater than the ambient light threshold. In step S4, the smoke concentration value is less than the smoke concentration threshold, the air quality value is less than the air quality threshold, the ambient temperature and humidity value is less than the ambient temperature and humidity threshold, and the ambient light value is greater than the ambient light threshold.

4. The intelligent building control method based on field bus according to claim 1 is characterized in that: The user terminal includes a mobile phone App, warning information is prompted through the mobile phone App, and the user views the internal environment information of the building through the mobile phone App. The administrator uses the user terminal to effectively check whether the user's authority is legal.

5. A fieldbus-based intelligent building control system, the control system is used to implement a fieldbus-based intelligent building control method according to any one of claims 1 to 4, characterized in that: It includes an acquisition module, a positioning module, a communication module and a processing module; The acquisition module acquires building environment to generate environment values, which are wirelessly sent to the processing module through the communication module. After the processing module fuses and compresses the environment values, it compares the environment values ​​with the reference values ​​and issues corresponding instructions based on the comparison results. The positioning module locates the building area where the acquisition module is located based on the environment values. The processing module performs numerical fusion based on BP neural network, including the following steps: According to the DCHP clustering routing protocol, the sensor network is clustered and the first cluster head and the second cluster head are selected; The first cluster head sends its own information and the information of the nodes in the cluster to the sink node, and the sink node constructs a BP neural network based on the information sent; Using the member nodes in the cluster and the first cluster head to perform fusion operations; The sensor nodes of each cluster process the collected numerical information and send the results to the first cluster head node for further fusion processing according to the neuron functions of the hidden layer and the output layer; The first cluster head sends the fused feature value to the adjacent second cluster head, which then sends it to the sink node via multi-hop routing; The processing module compresses the numerical value of the wireless sensor including the following steps: When a non-Sink node generates a numerical stream, let n0 be the node number; Let S<x[t1],x[t2],x[t3],...> The time series of monitoring values ​​generated by node n0; When the first numerical point (t1, x[t1]) arrives, store it. When the second numerical point (t2, x[t2]) arrives, find a line segment so that the vertical distance from the two numerical points to this line segment is ≤ ε. When the third value point (t3, x[t3]) arrives, check the first three value points; The first three numerical points can be described by a line segment and meet the ε error limit requirement, waiting for the arrival of the fourth numerical point; If the first three numerical points cannot be described by a line segment, describe the first and second numerical points with a line segment, and then search for a new line segment starting from the third numerical point; Whenever a new value point arrives, a check operation is performed until the end of the time series; The positioning module includes the following steps in locating the building area: Get the starting hop count: The anchor node broadcasts information to other nodes, and the message contains a hop counter Hops i , the initial value is 0, count the starting hops from the anchor node, and increase the hop count by 1 for each broadcast message received. i When the anchor node information is obtained, the starting hop count is taken as the starting hop count to the anchor node; Estimated distance: The unknown node estimates the average distance of one hop by using the starting hop number obtained in the first step and the known anchor node coordinates in the network. The calculation formula is: Where: (x i ,y i ),(x j ,y j ) represents the coordinates of anchor nodes i and j, h i is the starting hop number between anchor nodes i and j, and the average hop distance is represented by HopSize. Then the distance d between node o and anchor node i is o,i By d o,i =HopSize×h o,i Formula calculation.

6. The fieldbus-based intelligent building control system according to claim 5, characterized in that: The acquisition module includes a smoke sensor, an air quality sensor, a temperature and humidity sensor, and a light sensor. The smoke sensor is used to detect the smoke concentration in the environment, the air quality sensor is used to detect the air quality, the temperature and humidity sensor is used to detect the ambient temperature and humidity, and the light sensor is used to detect the ambient brightness. The processing module compares the environmental value with the reference value based on the PID, and issues corresponding instructions according to the comparison result. The calculation formula is: e(t)=r(t)-y(t) Among them, r(t) is the input set by the system, y(t) is the actual output value of the system, and e(t) represents the deviation between y(t) and r(t).

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