An emergency command and on-site dispatching system and method based on a heating network model

Abstract

The present invention relates to the technical field of operation status scheduling, and discloses an emergency command and on-site scheduling system and method based on a heat network model. The present invention first calculates the flow rate, pressure and temperature of the heating pipeline, establishes a heat network model, and commands and controls the pressure, temperature and flow rate of the heating pipeline in real time based on abnormal data; then uses a BP neural network to predict the pressure, temperature and flow rate of the heating pipeline to obtain heating pipeline prediction data; then, the heating pipeline leakage point is identified and verified using the heat network model heating pipeline prediction data, and the honey badger optimization algorithm is used to optimize the power emergency material delivery path to ensure the efficiency of material scheduling; finally, a thermal scheduling command platform and a thermal database are established to facilitate the control and storage of heating pipeline temperature, pressure, flow and leakage point data for future inquiries.

Description

Technical Field

[0001] The present invention relates to the technical field of operation status scheduling, and in particular to an emergency command and on-site scheduling system and method based on a heat network model. Background Art

[0002] The Chinese invention patent application with publication number CN117350445A discloses an artificial intelligence-based intelligent emergency command system and method. Specifically, the system comprises the following steps: first, obtaining the location where a dangerous situation occurs under the management of a power plant monitoring platform, receiving a material dispatch request from the location where the dangerous situation occurs, then obtaining traffic information for the location where the dangerous situation occurs and the location where the dangerous situation does not occur under the management of the power plant monitoring platform, generating an emergency material dispatch route corresponding to the traffic information, and marking the emergency material dispatch route; secondly, for the marked emergency material dispatch route, identifying the characteristic distribution of the emergency material dispatch route based on the impact of the dangerous situation, calculating the characteristic distribution of the emergency material dispatch route to obtain a first characteristic index; calculating the characteristic distribution of the emergency material dispatch route based on the characteristic distribution of different dangerous situations occurring along the emergency material dispatch route, and calculating a second characteristic index; based on the first and second characteristic indices, evaluating the emergency material dispatch route for a warning index, and sending the warning index to the emergency command personnel to assist the emergency command personnel in completing the dredging and maintenance of the emergency material dispatch route within the power plant and arranging personnel.

[0003] When a power plant emergency occurs, the alarm takes a long time to be received, and the emergency response from the relevant departments is delayed. This can lead to delayed handling of the emergency, which can lead to chain reactions. Traditional on-site dispatch for emergency incidents lacks a planned material dispatch route, a suitable platform for emergency dispatch, and support from advanced technologies such as big data. This can lead to delayed and chaotic on-site dispatch. Summary of the Invention

[0004] In response to the problems in the related art, the present invention provides an emergency command and on-site dispatching system and method based on a heating network model to overcome the above-mentioned technical problems existing in the existing related art.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is an emergency command and on-site dispatch method based on a heat network model, comprising the following steps:

[0007] S1. Obtain the heating data of the power plant in the heating network model, generate the heating pipeline matrix of the power plant, establish a heating network model based on the dynamic characteristics of the heating network, realize real-time control of the pressure, temperature and flow of the heating pipeline, and obtain a processed heating network model;

[0008] S2. Obtaining pressure, temperature, and flow data of the heating pipeline according to the processed heating network model, recording the data as the heating pipeline data set, training a BP neural network to obtain a trained BP neural network, and optimizing the trained BP neural network using a genetic algorithm to predict the pressure, temperature, and flow of the heating pipeline, thereby obtaining heating pipeline prediction data;

[0009] S3. Obtain suspected heating pipeline leakage points based on the processed heating network model and heating pipeline prediction data, process the monitoring images using image recognition technology, verify the suspected heating pipeline leakage points, and obtain the heating pipeline leakage points. If a heating pipeline leakage point is found, plan an emergency power supply delivery route, repair the heating pipeline leakage point, and use the Honey Badger optimization algorithm to optimize the emergency power supply delivery route;

[0010] S4. Establish a thermal dispatching command platform and thermal database to display and store the temperature, pressure, flow and leakage point data of the heating pipeline in real time.

[0011] The invention first establishes a heating pipeline matrix of a power plant, numbers the heating pipelines, and then calculates the flow, pressure and temperature of each section of the heating pipeline, establishes a heating network model based on the dynamic characteristics of the heating network, and adjusts the pressure, temperature and flow of the heating pipeline in real time according to abnormal data, and comprehensively considers various factors to build a model to obtain more accurate results; secondly, the pressure, temperature and flow data of the heating pipeline are obtained according to the heating network model, and the BP neural network is trained to obtain a trained BP neural network, and the trained BP neural network is optimized using a genetic algorithm. By calculating the variable mutation probability, the population gradually converges to the optimal solution, and the pressure, temperature and flow of the heating pipeline are predicted, and the heating pipeline prediction data is obtained. The optimized BP neural network converges faster and the predicted value is closer to Actual value; then the suspected heating pipeline leakage point is obtained from the heating network model and the heat pipeline prediction data, and the suspected heating pipeline leakage point matrix is ​​generated from the monitoring image. First, the grayscale value is linearly processed to increase the contrast, and then the image is segmented. The pixel grayscale value is replaced based on the region growing principle. Finally, the Canny algorithm is used to extract the edge of the heating pipeline leakage point, and the heating pipeline leakage point identification and verification is completed. After the heating pipeline leakage point is located, the power emergency material delivery route is planned; the honey badger optimization algorithm is used to optimize the power emergency material delivery route. The honey badger finds the nectar source according to the strength of the smell and continuously updates the iterative delivery position. The algorithm converges quickly and has strong development capabilities; finally, a thermal dispatch command platform and a thermal database are established to facilitate the regulation and storage of heating pipeline temperature, pressure, flow and leakage point data.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11, obtain the heating pipeline data of the power plant in the heating network model, number the heating pipelines in sequence, and record them as the heating pipeline set A = {a1, a2, a3, ..., a i}, where a i Representing the heating pipe numbered i, assuming that each heating pipe in the heating pipe set has j pipe nodes, the power plant heating pipe matrix B is generated as follows:

[0014]

[0015] Among them, a ij represents the j-th pipeline node of the heating pipeline numbered i;

[0016] S12. Select a row in the power plant heating pipeline matrix as a heating pipeline parameter set. in Indicates the number The j-th pipe node of the heating pipe; set the specific friction resistance of the heating pipe to b, the length of the heating pipe to c, the resistance equivalent length to b', the absolute roughness of the heating pipe to c', the heat transfer medium density to b", the heating pipe diameter to c", the heating pipe resistance coefficient to α, the heating pipe local resistance coefficient to β, and number The pressure loss of the heating pipe is C1, numbered The heating pipe flow is C2, and the calculation formula is as follows:

[0017]

[0018] Assume that the flow rate of the i1th pipeline node of the heating pipeline is The number is The heating pipe flow is C2, and the calculation formula is as follows:

[0019]

[0020] Set the upper and lower limits of the water supply temperature in the heating pipe to be d′ max and d′ min The upper and lower limits of the return water temperature of the heating pipe are d″ max and d″ min , the water temperature of the heating pipe is d′ and d′ min <d′<d′ max , the return water temperature of the heating pipe is d″ and d″ min <d″<d″ max , the specific heat capacity of the heat transfer medium is e', numbered The hot water flow rate of the jth pipe node of the heating pipe is e″, then the number is The calculation formula for the thermal power C′ of the jth pipeline node of the heating pipeline is as follows:

[0021] C′=e′e″·(d′-d″);

[0022] Set the number to The outlet temperature of the heating pipe is d1, numbered The inlet temperature of the heating pipe is d2, numbered The calculation formula of the heating pipe temperature C3 is as follows:

[0023]

[0024] S13, the number is The heating pipe pressure and number are The heating pipe flow and number are The temperature of the heating pipe is obtained, the pressure of the heating network pipe, the flow rate of the heating network pipe and the temperature of the heating network pipe are obtained, and a heating network model is established; the pressure of the heating pipe to be regulated, the temperature of the heating pipe to be regulated and the flow rate of the heating pipe to be regulated are obtained from the heating network model;

[0025] Set the upper pressure threshold to ξ1, the lower pressure threshold to ξ2, the upper flow threshold to ψ1, the lower flow threshold to ψ2, the upper temperature threshold to ζ1, and the lower temperature threshold to ζ2; when the pressure of the heating pipe to be regulated is less than ξ2, the temperature of the heating pipe to be regulated and the flow of the heating pipe to be regulated are adjusted until the pressure of the heating pipe to be regulated is between ξ1 and ξ2; when the flow of the heating pipe to be regulated is less than ψ2, increase the pressure of the heating pipe to be regulated and the temperature of the heating pipe to be regulated until the flow of the heating pipe to be regulated is between ψ1 and ψ2; when the temperature of the heating pipe to be regulated is less than ζ2, increase the pressure of the heating pipe to be regulated and the flow of the heating pipe to be regulated until the temperature of the heating pipe to be regulated is between ζ1 and ζ2; realize real-time regulation of the pressure, temperature and flow of the heating pipe, and obtain a processed heat network model.

[0026] This invention obtains the power plant's heating pipeline data, establishes a power plant heating pipeline matrix, calculates the heating pipeline flow, heating pipeline pressure and heating pipeline temperature, establishes a heating network model based on the dynamic characteristics of the heating network, and adjusts the pressure, temperature and flow of the heating pipeline in real time according to abnormal data. The modeling takes into account various factors comprehensively to obtain more accurate results.

[0027] Preferably, said S2 comprises the following steps:

[0028] S21, the pressure, temperature and flow data of the heating pipeline are obtained according to the processed heat network model, and the heating pipeline data set A2 = {(a′1, a″1, a″′1), (a′2, a″2, a″′2), (a′3, a″3, a″′3), ..., (a′ e ,a″ e ,a″′e )}, where a e Indicates the pressure of the heating pipe numbered e, a″ e Indicates the temperature of the heating pipe numbered e, a″′ e Represents the flow of the heating pipe numbered e; the heating pipe data set is divided into a heating pipe training set and heating pipe test set in Indicates the number The heating pipe pressure, Indicates the number The heating pipe temperature, Indicates the number The flow rate of heating pipes;

[0029] Set the number of nodes in the input layer of the BP (multi-layer forward error back propagation) neural network to The number of output layer nodes is 3, the learning rate is χ and χ = 0.7, the hidden layer activation function is the tansig function, the heating pipe training set is normalized to obtain a normalized heating pipe training set, the normalized heating pipe training set is input into the BP neural network, and the network error is set to Continuously iterate, when the BP neural network error is less than When , stop the iteration and get the trained BP neural network; otherwise repeat S21 until the BP neural network error is less than

[0030] S22. Input the heating pipeline test set into the trained BP neural network, set the current number of iterations and the maximum number of iterations D, and iterate continuously. When the current number of iterations is greater than D, stop iterating to obtain the final BP neural network; otherwise, use the genetic algorithm to optimize the trained BP neural network to obtain the final BP neural network. The specific process is as follows:

[0031] S221, set the fitness function f of the trained BP neural network to calculate the formula as follows:

[0032]

[0033] in, Indicates the actual value, Represents the predicted value of the trained BP neural network,

[0034] Set the population size to δ, use the selection function to select the best individual in the population with a probability of e′, calculate the fitness function value of the individual in the population, and record it as the fitness function set A5 = {e1, e2, e3, ..., e δ}, where e δRepresents the fitness function value of the δth individual; sort the fitness function set to obtain the processed fitness function value set, and then use the distribution parameter to calculate the individual selection probability. The calculation formula is as follows:

[0035]

[0036] Where f′ represents the distribution parameter, e″ represents the individual selection probability;

[0037] The population is cross-operated. The process of cross-operation is to optimize the weights and biases of the trained BP neural network. Two individuals D1 and D2 are randomly selected from the population as parents. A random value f″ in the range of [0,1] is generated for the parents. The random value is used to perform an interpolation operation on the parents to generate two offspring individuals D1′ and D2′. The calculation formula is as follows:

[0038] D1′=D1f″+D2(1-f″), D2′=D1(1-f″)+D2f″;

[0039] The population is mutated. The process of population mutation is to narrow the local search range of the trained BP neural network. The current generation of the population is set to D′, the maximum generation of the population is set to D″, the population selects the mutation direction and starts mutation, and the mutated individuals are obtained. The mutation probability e″′ is calculated as follows:

[0040]

[0041] S222, the population selects the best individual of the first iteration with the probability of the best individual, and the trained BP neural network performs the first iteration operation. After the best individual of the first iteration is subjected to a crossover operation, the weight and bias of the trained BP neural network are adjusted; the best individual of the second iteration is selected from the best individual of the first iteration, and the trained BP neural network performs the second iteration operation; S222 is repeated until the current iteration number D′ of the trained BP neural network is greater than D″, and the iteration is stopped to obtain the final BP neural network;

[0042] S23. Input the historical heating pipeline pressure, temperature and flow data as the historical heating pipeline data set into the final BP neural network, and the final BP neural network outputs the predicted values ​​of the heating pipeline pressure, temperature and flow to obtain the heating pipeline prediction data.

[0043] This invention obtains the pressure, temperature and flow data of the heating pipeline through the heat network model, records it as the heating pipeline data set, trains the BP neural network to obtain a trained BP neural network, and uses a genetic algorithm to optimize the trained BP neural network. By realizing a variable mutation probability, the population gradually converges to the optimal solution, realizes the prediction of the pressure, temperature and flow of the heating pipeline, and obtains the heating pipeline prediction data. The optimized BP neural network converges faster, and the predicted value is closer to the actual value, achieving good results.

[0044] Preferably, the step S3 includes the following steps:

[0045] S31. Calculate a first suspected heating pipe leakage point based on the processed heating network model, and then obtain a second suspected heating pipe leakage point based on the heating pipe prediction data. Record the suspected heating pipe leakage point where the first suspected heating pipe leakage point and the second suspected heating pipe leakage point overlap as the suspected heating pipe leakage point. Obtain a monitoring image of the suspected heating pipe leakage point, set the horizontal dimension of the pixel points of the monitoring image of the suspected heating pipe leakage point to i′, and the vertical dimension of the pixel points of the monitoring image of the suspected heating pipe leakage point to j′, and generate a suspected heating pipe leakage point matrix B′ as follows:

[0046]

[0047] Among them, a′ i′j′ Represents the pixel points of the horizontal i′ dimension and vertical j′ dimension in the matrix of suspected heating pipeline leakage points;

[0048] S32, perform grayscale linear processing on the matrix of the suspected heating pipeline leakage points, set the grayscale transformation slope to g, the grayscale value to h, and the grayscale transformation range to Grayscale value conversion function Calculating a grayscale value after linear processing of the grayscale value, and when the grayscale value of a pixel in the suspected heating pipe leakage point matrix is ​​less than the grayscale value after linear processing of the grayscale value, replacing the grayscale value of the pixel in the suspected heating pipe leakage point matrix with the grayscale value after linear processing of the grayscale value; otherwise, retaining the grayscale value of the pixel in the suspected heating pipe leakage point matrix to obtain a processed suspected heating pipe leakage point matrix;

[0049] S33, performing image segmentation on the pixels in the processed matrix of suspected heating pipe leakage points, calculating the absolute value of the difference between the grayscale value of the pixel in the processed matrix of suspected heating pipe leakage points and the grayscale value of surrounding pixels, setting a fourth threshold as ω4, and when the absolute value of the difference between the grayscale value of the pixel in the processed matrix of suspected heating pipe leakage points and the grayscale value of surrounding pixels is less than ω4, recording the corresponding surrounding pixels as seed pixels;

[0050] Calculate the absolute value of the difference between the grayscale value of the seed pixel and the grayscale value of the pixels around the seed pixel, and then calculate the average of the grayscale value of the seed pixel and the grayscale value of the pixels around the seed pixel, which is recorded as Set the grayscale value of the seed pixel and the grayscale value of the pixels around the seed pixel to h′, and the grayscale value threshold ξ is calculated as follows:

[0051]

[0052] When the absolute value of the difference between the grayscale value of the seed pixel and the grayscale value of the pixels surrounding the seed pixel is less than ξ, the pixels surrounding the corresponding seed pixel are recorded as new seed pixels; otherwise, repeat until S33 to obtain a new seed pixel; a first cumulative grayscale histogram h″ is obtained from the grayscale values ​​of the pixels in the processed suspected heating pipe leakage point matrix and the grayscale values ​​of the surrounding pixels, and a first cumulative grayscale histogram h″′ is obtained from the grayscale value of the seed pixel and the grayscale values ​​of the pixels surrounding the seed pixel. The grayscale histogram threshold is set to ψ, and when max|h″-h″′| is less than ψ, the image segmentation is stopped; otherwise, the image segmentation is continued until max|h″-h″′| is less than ψ, and the image segmentation matrix of suspected heating pipe leakage points is obtained;

[0053] S34. Use the Canny algorithm to extract edges, establish a rectangular coordinate system XOY for the suspected heating pipe leakage point matrix of the image segmentation, and the coordinate origin O is the pixel point at the lower left corner of the suspected heating pipe leakage point matrix of the image segmentation. Calculate the partial derivatives of the suspected heating pipe leakage point matrix of the image segmentation on the X-axis and Y-axis to obtain the partial derivative matrix X' and the partial derivative matrix Y', then calculate the X-axis gradient amplitude and X-axis gradient direction of the partial derivative matrix X', and calculate the Y-axis gradient amplitude and Y-axis gradient direction of the partial derivative matrix Y' to obtain the image The gradient amplitude and gradient direction of the segmented suspected heating pipe leakage point matrix; comparing the grayscale value and gradient amplitude of the pixel points of the image segmented suspected heating pipe leakage point matrix along the gradient direction; when the grayscale value of the pixel point of the image segmented suspected heating pipe leakage point matrix is ​​greater than the gradient amplitude, retaining the corresponding pixel point of the image segmented suspected heating pipe leakage point matrix; otherwise, setting the grayscale value of the pixel point of the image segmented suspected heating pipe leakage point matrix to 0, thereby obtaining a processed suspected heating pipe leakage point matrix;

[0054] S35. Locate the leakage area using the processed matrix of suspected heating pipe leakage points, acquire the next frame of the monitoring image of the suspected heating pipe leakage point, obtain the processed matrix of suspected heating pipe leakage points of the next frame, and check whether the leakage area exists. If the leakage area exists, the suspected heating pipe leakage point is the heating pipe leakage point; otherwise, the suspected heating pipe leakage point is not the heating pipe leakage point;

[0055] After discovering a leak in the heating pipe, locate the leak according to the monitoring location, plan a route for the delivery of emergency power supplies, inspect and repair the leak, and use the Honey Badger optimization algorithm to optimize the route for the delivery of emergency power supplies. The specific steps are as follows:

[0056] S351. Set the starting point of the power emergency material delivery route to the initial position of the honey badger, the delivery cost is k1, the terrain cost is k2, the boundary cost is k3, and the fitness function of the honey badger optimization algorithm is F = min(k1+k2+k3); the honey badger collects honey, set the random number k′ and k′∈[0,1], the upper bound of the honey badger activity area is l1, and the lower bound of the honey badger activity area is l2. Then the calculation formula of the honey badger initial position x1 is as follows:

[0057] x1=l2+k′(l1-l2)

[0058] When a honey badger searches for nectar, the nectar emits fragrance, which generates a fragrance density factor. A random number k″ is set, and k″∈[0,1]. The intensity between the honey badger and the nectar source is m, and the distance between the honey badger and the nectar source is n. The formula for calculating the honey badger's olfactory intensity G is as follows:

[0059]

[0060] As time t increases during the honey badger's search for nectar, the fragrance density factor changes continuously, and the power emergency material delivery path changes direction continuously. The fragrance density factor calculation formula is as follows:

[0061]

[0062] Where F′ represents the fragrance density factor, k″′ represents a random number and k″′≥1, t max Indicates the maximum number of iterations;

[0063] S352. Determine the direction of the maximum fragrance density factor, which is the direction of the power emergency material delivery path, denoted as m'. During the honey badger's excavation of the nectar source, the honey badger changes its position to obtain a new position. The first delivery position x2 is obtained according to the direction of the power emergency material delivery path. The calculation formula is as follows:

[0064]

[0065] Among them, x′ represents the location of the nectar source, represents the predation ability of the honey badger, p1, p2 and p3 represent random numbers between 0 and 1;

[0066] The honey badger continues to search for honey sources and constantly updates the direction of the power emergency material delivery route to obtain a new delivery position, and obtains the second delivery position x3. The calculation formula is as follows:

[0067] x3=x′+m′·F′·p4·n

[0068] Among them, p4 represents a random number between 0 and 1;

[0069] The honey badger continuously searches for honey sources, continuously updates the delivery location, and updates the fitness function value under the power emergency material delivery path. It continuously iterates according to the fitness function value under the power emergency material delivery path. The current number of iterations is set to time t. When the current number of iterations is greater than t max , stop the iteration, and the honey badger's path to find the nectar source is the optimized power emergency material delivery path; otherwise, repeat S321 and S322 until the current number of iterations is greater than t max .

[0070] This invention generates a matrix of suspected heating pipe leakage points through monitoring images, first performs linear processing of grayscale values ​​to increase contrast, and then uses the Canny algorithm to extract the edges of the heating pipe leakage points after image segmentation and region growth principle processing, completing the identification and verification of the heating pipe leakage points. After locating the heating pipe leakage points, it begins to plan the route for the delivery of emergency power supplies; uses the honey badger optimization algorithm to optimize the route for the delivery of emergency power supplies, and uses the honey badger to find the nectar source based on the strength of the smell and continuously updates the iterative delivery position. The algorithm has a fast convergence speed and strong development capabilities.

[0071] Preferably, the S4 comprises the following steps:

[0072] S41. Establish a heat dispatching command platform to display the temperature, pressure, and flow data of the heating pipeline in real time. When the temperature, pressure, and flow data of the heating pipeline are abnormal, the temperature, pressure, and flow data of the heating pipeline are adjusted in real time. When a leakage point in the heating pipeline is found, the location of the leakage point of the heating pipeline is located and the leakage point of the heating pipeline is inspected and repaired according to the optimized power emergency material delivery route.

[0073] S42. Establish a thermal database to store past heating pipeline temperature, pressure, flow, heating pipeline prediction data and leakage point data.

[0074] This embodiment also discloses an emergency command and on-site dispatch system based on a heat network model, which specifically includes: a heat network model establishment module, a heat supply pipeline data control module, a heat supply pipeline data prediction module, a heat pipeline leakage point location module, and an electric power emergency material transportation path optimization module;

[0075] The heat network model building module is used to build the pressure, flow and temperature models of the heating pipeline;

[0076] The heating pipeline data control module is used to use the model to calculate the pressure, flow and temperature of the heating pipeline and perform real-time control on abnormal situations;

[0077] The heating pipeline data prediction module is used to train the BP neural network and use the BP neural network to predict the heating pipeline data;

[0078] The heating pipeline leakage point locating module is used to detect leakage points on monitoring images using image processing to verify the location of suspected heating pipeline leakage points;

[0079] The power emergency material delivery path optimization module is used to optimize the power emergency material delivery path using the honey badger optimization algorithm.

[0080] The present invention has the following beneficial effects:

[0081] 1. This invention obtains the heating pipeline data of the power plant, establishes the heating pipeline matrix of the power plant, calculates the flow rate, pressure and temperature of the heating pipeline, establishes a heating network model based on the dynamic characteristics of the heating network, and adjusts the pressure, temperature and flow rate of the heating pipeline in real time according to abnormal data. The model is built by comprehensively considering various factors to obtain more accurate results.

[0082] 2. This invention obtains the pressure, temperature and flow data of the heating pipeline through the heat network model, trains the BP neural network to obtain a trained BP neural network, and uses a genetic algorithm to optimize the trained BP neural network. By realizing a variable mutation probability, the population gradually converges to the optimal solution, thereby realizing the prediction of the pressure, temperature and flow of the heating pipeline. The optimized BP neural network converges faster, and the predicted value is closer to the actual value.

[0083] 3. The invention generates a matrix of suspected heating pipe leakage points through monitoring images, first performs grayscale value linear processing, and then uses image segmentation and region growth principle processing, and uses the Canny algorithm to extract the edges of the heating pipe leakage points to complete the identification and verification of the heating pipe leakage points. After locating the heating pipe leakage points, the honey badger optimization algorithm is used to optimize the power emergency material delivery path. The honey badger finds the nectar source according to the strength of the smell and continuously updates the iterative delivery position. The algorithm has a fast convergence speed and strong development capabilities.

[0084] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying any creative work.

[0086] Figure 1The present invention provides a flow chart of emergency command and on-site dispatching by an emergency command and on-site dispatching system based on a heat network model. DETAILED DESCRIPTION

[0087] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0088] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0089] This embodiment discloses an emergency command and on-site dispatch method based on a heat network model, which specifically includes the following contents:

[0090] S1. Obtain the heating data of the power plant in the heating network model, generate the heating pipeline matrix of the power plant, establish a heating network model based on the dynamic characteristics of the heating network, realize real-time control of the pressure, temperature and flow of the heating pipeline, and obtain a processed heating network model;

[0091] Said S1 comprises the following steps:

[0092] S11, obtain the heating pipeline data of the power plant in the heating network model, number the heating pipelines in sequence, and record them as the heating pipeline set A = {a1, a2, a3, ..., a i}, where a i Representing the heating pipe numbered i, assuming that each heating pipe in the heating pipe set has j pipe nodes, the power plant heating pipe matrix B is generated as follows:

[0093]

[0094] Among them, a ij represents the j-th pipeline node of the heating pipeline numbered i;

[0095] S12. Select a row in the power plant heating pipeline matrix as a heating pipeline parameter set. in Indicates the number The j-th pipe node of the heating pipe; set the specific friction resistance of the heating pipe to b, the length of the heating pipe to c, the resistance equivalent length to b', the absolute roughness of the heating pipe to c', the heat transfer medium density to b", the heating pipe diameter to c", the heating pipe resistance coefficient to α, the heating pipe local resistance coefficient to β, and number The pressure loss of the heating pipe is C1, numbered The heating pipe flow is C2, and the calculation formula is as follows:

[0096]

[0097] Assume that the flow rate of the i1th pipeline node of the heating pipeline is The number is The heating pipe flow is C2, and the calculation formula is as follows:

[0098]

[0099] Set the upper and lower limits of the water supply temperature in the heating pipe to be d′ max and d′ min The upper and lower limits of the return water temperature of the heating pipe are d″ max and d″ min , the water temperature of the heating pipe is d′ and d′ min <d′<d′ max , the return water temperature of the heating pipe is d″ and d″ min <d″<d″ max , the specific heat capacity of the heat transfer medium is e', numbered The hot water flow rate of the jth pipe node of the heating pipe is e″, then the number is The calculation formula for the thermal power C′ of the jth pipeline node of the heating pipeline is as follows:

[0100] C′=e′e″·(d′-d″);

[0101] Set the number to The outlet temperature of the heating pipe is d1, numbered The inlet temperature of the heating pipe is d2, numbered The calculation formula of the heating pipe temperature C3 is as follows:

[0102]

[0103] S13, the number is The heating pipe pressure and number are The heating pipe flow and number are The temperature of the heating pipe is obtained, the pressure of the heating network pipe, the flow rate of the heating network pipe and the temperature of the heating network pipe are obtained, and a heating network model is established; the pressure of the heating pipe to be regulated, the temperature of the heating pipe to be regulated and the flow rate of the heating pipe to be regulated are obtained from the heating network model;

[0104] Set the upper pressure threshold to ξ1, the lower pressure threshold to ξ2, the upper flow threshold to ψ1, the lower flow threshold to ψ2, the upper temperature threshold to ζ1, and the lower temperature threshold to ζ2; when the pressure of the heating pipe to be regulated is less than ξ2, the temperature of the heating pipe to be regulated and the flow of the heating pipe to be regulated are adjusted until the pressure of the heating pipe to be regulated is between ξ1 and ξ2; when the flow of the heating pipe to be regulated is less than ψ2, increase the pressure of the heating pipe to be regulated and the temperature of the heating pipe to be regulated until the flow of the heating pipe to be regulated is between ψ1 and ψ2; when the temperature of the heating pipe to be regulated is less than ζ2, increase the pressure of the heating pipe to be regulated and the flow of the heating pipe to be regulated until the temperature of the heating pipe to be regulated is between ζ1 and ζ2; realize real-time regulation of the pressure, temperature and flow of the heating pipe, and obtain a processed heat network model;

[0105] S2. Obtaining the pressure, temperature, and flow data of the heating pipeline from the processed heating network model, recording the data as the heating pipeline data set, training a BP neural network to obtain a trained BP neural network, and optimizing the trained BP neural network using a genetic algorithm to predict the pressure, temperature, and flow of the heating pipeline, thereby obtaining heating pipeline prediction data;

[0106] The S2 comprises the following steps:

[0107] S21, the processed heat network model obtains the pressure, temperature and flow data of the heating pipeline, and obtains the heating pipeline data set A2 = {(a′1, a″1, a″′1), (a′2, a″2, a″′2), (a′3, a″3, a″′3), ..., (a′ e ,a″ e ,a″′ e )}, where a′ e Indicates the pressure of the heating pipe numbered e, a″ e Indicates the temperature of the heating pipe numbered e, a″′ e Represents the flow of the heating pipe numbered e; the heating pipe data set is divided into a heating pipe training set and heating pipe test set in Indicates the number The heating pipe pressure, Indicates the number The heating pipe temperature, Indicates the number The flow rate of heating pipes;

[0108] Set the number of nodes in the BP neural network input layer to The number of output layer nodes is 3, the learning rate is χ and χ = 0.7, the hidden layer activation function is the tansig function, the heating pipe training set is normalized to obtain a normalized heating pipe training set, the normalized heating pipe training set is input into the BP neural network, and the network error is set to Continuously iterate, when the BP neural network error is less than When , stop the iteration and get the trained BP neural network; otherwise repeat S21 until the BP neural network error is less than

[0109] S22. Input the heating pipeline test set into the trained BP neural network, set the current number of iterations and the maximum number of iterations D, and iterate continuously. When the current number of iterations is greater than D, stop iterating to obtain the final BP neural network; otherwise, use the genetic algorithm to optimize the trained BP neural network to obtain the final BP neural network. The specific process is as follows:

[0110] S221, set the fitness function f of the trained BP neural network to calculate the formula as follows:

[0111]

[0112] in, Indicates the actual value, Represents the predicted value of the trained BP neural network,

[0113] Set the population size to δ, use the selection function to select the best individual in the population with a probability of e′, calculate the fitness function value of the individual in the population, and record it as the fitness function set A5 = {e1, e2, e3, ..., e δ}, where e δ Represents the fitness function value of the δth individual; sort the fitness function set to obtain the processed fitness function value set, and then use the distribution parameter to calculate the individual selection probability. The calculation formula is as follows:

[0114]

[0115] Where f′ represents the distribution parameter, e″ represents the individual selection probability;

[0116] The population is crossover operated. The process of crossover operation is to optimize the weights and biases of the trained BP neural network. Two individuals D1 and D2 are randomly selected from the population as parents. A random value f″ in the range of [0,1] is generated for the parents. The random value is used to perform interpolation operation on the parents to generate two offspring individuals D′1 and D′2. The calculation formula is as follows:

[0117] D′1=D1f″+D2(1-f″), D′2=D1(1-f″)+D2f″;

[0118] The population performs mutation operation, which is the process of narrowing the local search range of the trained BP neural network. Set the current generation of the population to D′, the maximum generation of the population to D″, the population selects the mutation direction and starts mutation, and obtains the mutated individuals. The mutation probability e″′ is calculated as follows:

[0119]

[0120] S222, the population selects the best individual of the first iteration with the probability of the best individual, and the trained BP neural network performs the first iteration operation. After the best individual of the first iteration is subjected to a crossover operation, the weight and bias of the trained BP neural network are adjusted; the best individual of the second iteration is selected from the best individuals of the first iteration, and the trained BP neural network performs the second iteration operation; S222 is repeated until the current iteration number D′ of the trained BP neural network is greater than D″, and the iteration is stopped to obtain the final BP neural network;

[0121] S23, inputting historical heating pipeline pressure, temperature, and flow data as a historical heating pipeline data set into the final BP neural network, and the final BP neural network outputs predicted values ​​of the heating pipeline pressure, temperature, and flow to obtain heating pipeline prediction data;

[0122] S3. Obtain suspected heating pipeline leakage points based on the processed heating network model and heating pipeline prediction data, process the monitoring images using image recognition technology, verify the suspected heating pipeline leakage points, and obtain the heating pipeline leakage points. If a heating pipeline leakage point is found, plan an emergency power supply delivery route, repair the heating pipeline leakage point, and use the Honey Badger optimization algorithm to optimize the emergency power supply delivery route;

[0123] The S3 includes the following steps:

[0124] S31. Calculate a first suspected heating pipe leakage point using the processed heating network model, and then obtain a second suspected heating pipe leakage point using the heating pipe prediction data. Record the suspected heating pipe leakage point where the first suspected heating pipe leakage point and the second suspected heating pipe leakage point overlap as the suspected heating pipe leakage point. Obtain a monitoring image of the suspected heating pipe leakage point, set the horizontal dimension of the pixel points of the monitoring image of the suspected heating pipe leakage point to i′, and the vertical dimension of the pixel points of the monitoring image of the suspected heating pipe leakage point to j′, and generate a suspected heating pipe leakage point matrix B′ as follows:

[0125]

[0126] Among them, a′ i′j′ Represents the pixel points of the horizontal i′ dimension and vertical j′ dimension in the matrix of suspected heating pipeline leakage points;

[0127] S32, perform grayscale linear processing on the matrix of the suspected heating pipeline leakage points, set the grayscale transformation slope to g, the grayscale value to h, and the grayscale transformation range to Grayscale value conversion function Calculating a grayscale value after linear processing of the grayscale value, and when the grayscale value of a pixel in the suspected heating pipe leakage point matrix is ​​less than the grayscale value after linear processing of the grayscale value, replacing the grayscale value of the pixel in the suspected heating pipe leakage point matrix with the grayscale value after linear processing of the grayscale value; otherwise, retaining the grayscale value of the pixel in the suspected heating pipe leakage point matrix to obtain a processed suspected heating pipe leakage point matrix;

[0128] S33, performing image segmentation on the pixels in the processed matrix of suspected heating pipe leakage points, calculating the absolute value of the difference between the grayscale value of the pixel in the processed matrix of suspected heating pipe leakage points and the grayscale value of surrounding pixels, setting a fourth threshold as ω4, and when the absolute value of the difference between the grayscale value of the pixel in the processed matrix of suspected heating pipe leakage points and the grayscale value of surrounding pixels is less than ω4, recording the corresponding surrounding pixels as seed pixels;

[0129] Calculate the absolute value of the difference between the grayscale value of the seed pixel and the grayscale value of the pixels around the seed pixel, and then calculate the average of the grayscale value of the seed pixel and the grayscale value of the pixels around the seed pixel, which is recorded as Set the grayscale value of the seed pixel and the grayscale value of the pixels around the seed pixel to h′, and the grayscale value threshold ξ is calculated as follows:

[0130]

[0131] When the absolute value of the difference between the grayscale value of the seed pixel and the grayscale value of the pixels surrounding the seed pixel is less than ξ, the pixels surrounding the corresponding seed pixel are recorded as new seed pixels; otherwise, repeat until S33 to obtain a new seed pixel; a first cumulative grayscale histogram h″ is obtained from the grayscale values ​​of the pixels in the processed suspected heating pipe leakage point matrix and the grayscale values ​​of the surrounding pixels, and a first cumulative grayscale histogram h″′ is obtained from the grayscale value of the seed pixel and the grayscale values ​​of the pixels surrounding the seed pixel. The grayscale histogram threshold is set to ψ, and when max|h″-h″′| is less than ψ, the image segmentation is stopped; otherwise, the image segmentation is continued until max|h″-h″′| is less than ψ, and the image segmentation matrix of suspected heating pipe leakage points is obtained;

[0132] S34, use the Canny algorithm to extract edges, establish a rectangular coordinate system XOY for the suspected heating pipe leakage point matrix of the image segmentation, the coordinate origin O is the pixel point in the lower left corner of the suspected heating pipe leakage point matrix of the image segmentation, calculate the partial derivatives of the suspected heating pipe leakage point matrix of the image segmentation on the X axis and Y axis to obtain the partial derivative matrix X' and the partial derivative matrix Y', then calculate the X-axis gradient amplitude and X-axis gradient direction of the partial derivative matrix X', calculate the Y-axis gradient amplitude and Y-axis gradient direction of the partial derivative matrix Y', and obtain the image segmentation. the gradient amplitude and gradient direction of the suspected heating pipe leakage point matrix of the image segmentation; comparing the grayscale value and gradient amplitude of the pixel points of the suspected heating pipe leakage point matrix of the image segmentation along the gradient direction; when the grayscale value of the pixel point of the suspected heating pipe leakage point matrix of the image segmentation is greater than the gradient amplitude, retaining the corresponding pixel point of the suspected heating pipe leakage point matrix of the image segmentation; otherwise, setting the grayscale value of the pixel point of the suspected heating pipe leakage point matrix of the image segmentation to 0, thereby obtaining a processed suspected heating pipe leakage point matrix;

[0133] S35. Locate the leakage area using the processed matrix of suspected heating pipe leakage points, acquire the next frame of the monitoring image of the suspected heating pipe leakage point, obtain the processed matrix of suspected heating pipe leakage points of the next frame, and check whether the leakage area exists. If the leakage area exists, the suspected heating pipe leakage point is the heating pipe leakage point; otherwise, the suspected heating pipe leakage point is not the heating pipe leakage point;

[0134] After discovering a leak in the heating pipe, locate the leak according to the monitoring location, plan a route for the delivery of emergency power supplies, inspect and repair the leak, and use the Honey Badger optimization algorithm to optimize the route for the delivery of emergency power supplies. The specific steps are as follows:

[0135] S351. Set the starting point of the power emergency material delivery route to the initial position of the honey badger, the delivery cost is k1, the terrain cost is k2, the boundary cost is k3, and the fitness function of the honey badger optimization algorithm is F = min(k1+k2+k3); the honey badger collects honey, set the random number k′ and k′∈[0,1], the upper bound of the honey badger activity area is l1, and the lower bound of the honey badger activity area is l2. Then the calculation formula of the honey badger initial position x1 is as follows:

[0136] x1=l2+k′(l1-l2)

[0137] When a honey badger searches for nectar, the nectar emits fragrance, which generates a fragrance density factor. A random number k″ is set, and k″∈[0,1]. The intensity between the honey badger and the nectar source is m, and the distance between the honey badger and the nectar source is n. The formula for calculating the honey badger's olfactory intensity G is as follows:

[0138]

[0139] As time t increases during the honey badger's search for nectar, the fragrance density factor changes continuously, and the power emergency material delivery path changes direction continuously. The fragrance density factor calculation formula is as follows:

[0140]

[0141] Where F′ represents the fragrance density factor, k″′ represents a random number and k″′≥1, t max Indicates the maximum number of iterations;

[0142] S352. Determine the direction of the maximum fragrance density factor, which is the direction of the power emergency material delivery path, denoted as m'. During the honey badger's excavation of the nectar source, the honey badger changes its position to obtain a new position. The first delivery position x2 is obtained according to the direction of the power emergency material delivery path. The calculation formula is as follows:

[0143]

[0144] Among them, x′ represents the location of the nectar source, represents the predation ability of the honey badger, p1, p2 and p3 represent random numbers between 0 and 1;

[0145] The honey badger continues to search for honey sources and constantly updates the direction of the power emergency material delivery route to obtain a new delivery position, and obtains the second delivery position x3. The calculation formula is as follows:

[0146] x3=x′+m′·F′·p4·n

[0147] Among them, p4 represents a random number between 0 and 1;

[0148] The honey badger continuously searches for honey sources, continuously updates the delivery location, and updates the fitness function value under the power emergency material delivery path. It continuously iterates according to the fitness function value under the power emergency material delivery path. The current number of iterations is set to time t. When the current number of iterations is greater than t max , stop the iteration, and the honey badger's path to find the nectar source is the optimized power emergency material delivery path; otherwise, repeat S321 and S322 until the current number of iterations is greater than t max ;

[0149] S4. Establish a thermal dispatching command platform and thermal database to display and store real-time data on heating pipeline temperature, pressure, flow and leakage points;

[0150] The S4 comprises the following steps:

[0151] S41. Establish a heat dispatching command platform to display the temperature, pressure, and flow data of the heating pipeline in real time. When the temperature, pressure, and flow data of the heating pipeline are abnormal, the temperature, pressure, and flow data of the heating pipeline are adjusted in real time. When a leakage point in the heating pipeline is found, the location of the leakage point of the heating pipeline is located and the leakage point of the heating pipeline is inspected and repaired according to the optimized power emergency material delivery route.

[0152] S42. Establish a thermal database to store past heating pipeline temperature, pressure, flow, heating pipeline prediction data and leakage point data.

[0153] This embodiment also discloses an emergency command and on-site dispatch system based on a heat network model, which specifically includes: a heat network model establishment module, a heat supply pipeline data control module, a heat supply pipeline data prediction module, a heat pipeline leakage point location module, and an electric power emergency material transportation path optimization module;

[0154] The heat network model building module is used to build the pressure, flow and temperature models of the heating pipeline;

[0155] The heating pipeline data control module is used to use the model to calculate the pressure, flow and temperature of the heating pipeline and perform real-time control on abnormal situations;

[0156] The heating pipeline data prediction module is used to train the BP neural network and use the BP neural network to predict the heating pipeline data;

[0157] The heating pipeline leakage point locating module is used to detect leakage points on monitoring images using image processing to verify the location of suspected heating pipeline leakage points;

[0158] The power emergency material delivery path optimization module is used to optimize the power emergency material delivery path using the honey badger optimization algorithm.

[0159] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0160] The preferred embodiments of the invention disclosed above are intended only to help illustrate the invention. These preferred embodiments do not exhaust all details, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. An emergency command and on-site dispatch method based on a heating network model, characterized in that: The steps include: S1. Obtain the heating data of the power plant in the heating network model, generate the heating pipeline matrix of the power plant, establish a heating network model based on the dynamic characteristics of the heating network, realize real-time control of the pressure, temperature and flow of the heating pipeline, and obtain a processed heating network model; S2. Obtaining pressure, temperature, and flow data of the heating pipeline according to the processed heating network model, recording the data as a heating pipeline data set, training a BP neural network to obtain a trained BP neural network, and optimizing the trained BP neural network to predict the pressure, temperature, and flow of the heating pipeline, thereby obtaining heating pipeline prediction data; S3. Determine suspected heating pipeline leakage points based on the processed heating network model and heating pipeline prediction data, process the monitoring images using image recognition technology, verify the suspected heating pipeline leakage points, and determine the heating pipeline leakage points. If a heating pipeline leakage point is found, plan an emergency power supply delivery route, inspect and repair the heating pipeline leakage point, and optimize the emergency power supply delivery route. The S3 comprises the following steps: S31, obtaining a suspected heating pipeline leakage point based on the processed heating network model and heating pipeline prediction data; obtaining a monitoring image of the suspected heating pipeline leakage point, processing the monitoring image using image recognition technology, verifying the suspected heating pipeline leakage point, and obtaining the heating pipeline leakage point; S32. After a leak in the heating pipe is discovered, the leak is located according to the monitoring position, and a route for the delivery of emergency power supplies is planned. The leak is repaired and the route for the delivery of emergency power supplies is optimized using the Honey Badger optimization algorithm. S4. Establish a thermal dispatching command platform and thermal database to display and store the temperature, pressure, flow and leakage point data of the heating pipeline in real time.

2. The method for emergency command and on-site dispatch based on a heat network model according to claim 1, characterized in that: The S1 comprises the following steps: S11. Obtain the heating pipeline data of the power plant in the heat network model and generate a heating pipeline matrix of the power plant; S12. Select a row in the heating pipeline matrix of the power plant as a set of heating pipeline parameters, calculate the flow, temperature and pressure of the heating pipeline, and establish a heating network model; obtain the pressure of the heating pipeline to be regulated, the temperature of the heating pipeline to be regulated and the flow of the heating pipeline to be regulated from the heating network model, and regulate the pressure of the heating pipeline to be regulated, the temperature of the heating pipeline to be regulated and the flow of the heating pipeline to be regulated in real time to obtain a processed heating network model.

3. The method for emergency command and on-site dispatch based on a heat network model according to claim 2, characterized in that: The S2 comprises the following steps: S21, obtain the pressure, temperature and flow data of the heating pipeline according to the processed heating network model, obtain the heating pipeline data set, and divide the heating pipeline data set into a heating pipeline training set and a heating pipeline test set; set the number of nodes in the BP neural network input layer to be , the number of output layer nodes is 3, and the learning rate is and , the hidden layer activation function is the tansig function, the heating pipe training set is normalized to obtain the normalized heating pipe training set, the normalized heating pipe training set is input into the BP neural network, and the network error is set to , iterate continuously, when the BP neural network error is less than When , the iteration is stopped and the trained BP neural network is obtained; Otherwise, repeat S21 until the BP neural network error is less than ; S22: Input the heating pipeline test set into the trained BP neural network, set the current number of iterations and the maximum number of iterations , iterate continuously, when the current number of iterations is greater than When , the iteration is stopped and the final BP neural network is obtained; otherwise, the genetic algorithm is used to optimize the trained BP neural network to obtain the final BP neural network; S23. Input the historical heating pipeline pressure, temperature and flow data as the historical heating pipeline data set into the final BP neural network, and the final BP neural network outputs the predicted values ​​of the heating pipeline pressure, temperature and flow to obtain the heating pipeline prediction data.

4. The method for emergency command and on-site dispatch based on a heat network model according to claim 3 is characterized in that: The S22 includes the following steps: S221, setting the fitness function of the trained BP neural network The calculation formula is as follows, in, Indicates the actual value, Represents the predicted value of the trained BP neural network, , Set the population size to , the probability of selecting the best individual in the population using the selection function is , calculate the fitness function value of individuals in the population, sort the fitness function values ​​of individuals in the population to obtain the processed fitness function value set, and then use the distribution parameters to calculate the individual selection probability. The calculation formula is as follows, ， in, represents the distribution parameter, represents the probability of individual selection; The population is cross-operated. The process of cross-operation is the process of optimizing the weights and biases of the trained BP neural network. Two individuals are randomly selected from the population. and As the parent, generate Random value in range , use random values ​​to interpolate the parent to generate 2 offspring individuals and , the calculation formula is as follows, ， ； The population is mutated. The process of population mutation is the process of narrowing the local search range of the trained BP neural network. Set the current generation of the population to , the maximum number of generations of the population is , the population selects the mutation direction and starts mutation, and obtains the mutated individuals, the mutation probability The calculation formula is as follows, 。 5. The method for emergency command and on-site dispatch based on a heat network model according to claim 4, characterized in that: The population selects the best individual of the first iteration with the probability of the best individual, and the trained BP neural network performs the first iteration operation at this time. After the best individual of the first iteration is subjected to the crossover operation, the weight and bias of the trained BP neural network are adjusted; the best individual of the second iteration is selected from the best individual of the first iteration, and the trained BP neural network performs the second iteration operation; S222 is repeated continuously until the current number of iterations of the trained BP neural network is reached. Greater than , stop the iteration and get the final BP neural network.

6. The method of emergency command and on-site dispatch based on a heat network model according to claim 5, characterized in that: The S32 includes the following steps: S321. Set the starting point of the power emergency material delivery route to the initial position of the honey badger, and the delivery cost is , the terrain cost is , the boundary cost is , the fitness function of the honey badger optimization algorithm ; Honey badger collects honey, set random number and , the upper limit of the honey badger activity area is , the lower limit of the honey badger's activity area is , then the initial position of the honey badger is The calculation formula is as follows, When the honey badger searches for nectar, the nectar emits fragrance to generate a fragrance density factor, and a random number is set. and , the intensity between honey badger and nectar source is , the distance between the honey badger and the nectar source is , honey badger's sense of smell The calculation formula is as follows, As time goes by, the honey badger searches for nectar sources. The fragrance density factor is constantly changing, and the power emergency material delivery path is constantly changing direction. The fragrance density factor calculation formula is as follows: in, represents the aroma density factor, represents a random number and , Indicates the maximum number of iterations; S322. Determine the direction of the maximum fragrance density factor, which is the direction of the power emergency material delivery path, and record it as During the honey badger's digging process, the honey badger changes its position to obtain a new position, and obtains the first delivery position according to the direction of the power emergency material delivery route. The honey badger continues to search for honey sources, constantly updates the direction of the power emergency material delivery route to obtain a new delivery location, and obtains the second delivery location .

7. The method of emergency command and on-site dispatch based on a heating network model according to claim 6, characterized in that: The honey badger continuously searches for honey sources, continuously updates the delivery location, and updates the fitness function value under the power emergency material delivery path. It continuously iterates according to the fitness function value under the power emergency material delivery path. The current number of iterations is the time , when the current iteration number is greater than , stop the iteration, and the honey badger's path to find the nectar source is the optimized power emergency material delivery path; otherwise, repeat S321 and S322 until the current number of iterations is greater than .

8. The method for emergency command and on-site dispatch based on a heating network model according to claim 7, characterized in that: The S4 comprises the following steps: S41. Establish a thermal dispatching and command platform to display the temperature, pressure and flow data of the heating pipeline in real time; establish a thermal database to store the past temperature, pressure, flow, heating pipeline prediction data and leakage point data of the heating pipeline.

9. An emergency command and on-site dispatch system based on a heating network model, capable of implementing an emergency command and on-site dispatch method based on a heating network model as claimed in any one of claims 1 to 8, characterized in that: Specifically include: heat network model building module, heat supply pipeline data control module, heat supply pipeline data prediction module, heat supply pipeline leakage point location module and power emergency material transportation route optimization module; The heat network model building module is used to build a pressure, flow and temperature model of the heating pipeline; The heating pipeline data control module is used to use the model to calculate the pressure, flow and temperature of the heating pipeline and perform real-time control on abnormal situations; The heating pipeline data prediction module is used to train the BP neural network and use the BP neural network to predict the heating pipeline data; The heating pipe leakage point positioning module is used to use image processing to detect leakage points on monitoring images and verify the location of suspected heating pipe leakage points; obtain the suspected heating pipe leakage points based on the processed heat network model and heating pipe prediction data; obtain monitoring images of suspected heating pipe leakage points, process the monitoring images through image recognition technology, verify the suspected heating pipe leakage points and obtain the heating pipe leakage points; after the heating pipe leakage points are found, locate the heating pipe leakage points based on the monitoring positions, plan the power emergency material delivery route, inspect and repair the heating pipe leakage points, and use the Honey Badger optimization algorithm to optimize the power emergency material delivery route; The power emergency material delivery path optimization module is used to optimize the power emergency material delivery path using the honey badger optimization algorithm.

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