Remote intelligent control method and system for concrete production equipment

Abstract

The invention provides a remote intelligent control method and system for concrete production equipment, and relates to the technical field of concrete production. Establishing an equipment association topological graph and determining a weight coefficient; dividing dynamic collaboration groups based on collaboration efficiency indexes; distributing tasks according to the production capacity indexes; and optimizing the control parameters through an ant colony algorithm to generate an optimal instruction sequence. According to the invention, efficient collaborative operation and intelligent management of concrete production equipment are realized, and the production efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete production, and in particular to a remote intelligent control method and system for concrete production equipment. Background Art

[0002] The existing remote control and management system for concrete production equipment has the following defects and deficiencies: the existing system lacks in-depth analysis and utilization of the correlation between concrete production equipment. Each equipment often operates relatively independently, and dynamic collaborative grouping based on correlation topology and weight coefficients cannot be achieved, resulting in low efficiency of collaboration between equipment, irrational resource allocation, and difficulty in coping with complex and changing production environments; the existing technology lacks a scientific and effective evaluation mechanism for production task allocation, and usually adopts a static or empirical task allocation method, failing to dynamically optimize the allocation based on the actual production capacity indicators of each equipment group, resulting in some equipment being overloaded while others are idle, and overall production efficiency is limited; the existing concrete production control system has a single technical means for control strategy optimization, and most of them adopt fixed parameter control or simple PID control. There is a lack of adaptive control strategy optimization mechanism based on multi-objective evaluation and intelligent algorithms, making it difficult to achieve dynamic optimization of control parameters and unable to automatically adjust the control strategy according to actual production conditions, thereby affecting the production quality and efficiency of concrete. Summary of the Invention

[0003] The embodiments of the present invention provide a remote intelligent control method and system for concrete production equipment, which can solve the problems in the prior art.

[0004] A first aspect of an embodiment of the present invention provides a remote intelligent control method for concrete production equipment, comprising: Collecting production parameter data of concrete production equipment; establishing a correlation topology map between the concrete production equipment based on the production parameter data, and determining correlation weight coefficients; calculating a coordination efficiency index of an equipment group based on the correlation weight coefficients, and dividing the concrete production equipment into a plurality of dynamic coordination groups based on the coordination efficiency index; Obtaining concrete production task instructions, and calculating the production capacity index of each dynamic coordination group based on the equipment distribution of the dynamic coordination group; Production tasks are assigned to each dynamic collaborative group according to the production capacity indicators, and a multi-objective evaluation score is calculated based on the completion status. The objective function of the ant colony algorithm is constructed based on the multi-objective evaluation score. The weight coefficients of the control parameters are used as decision variables for iterative optimization. The optimal weight coefficient combination obtained after iterative optimization is selected to generate the optimal control instruction sequence.

[0005] Establishing a correlation topology diagram among concrete production equipment according to the production parameter data and determining correlation weight coefficients includes: Calculate mutual information entropy values at different time delay orders according to the production parameter data, compose a multi-scale mutual information entropy sequence from the mutual information entropy values, and extract a local maximum point from the multi-scale mutual information entropy sequence as an optimal time delay order; Determine a state transition synchronization degree of the concrete production equipment according to a difference value of elements in a state transition probability matrix corresponding to a state transition frequency of the concrete production equipment in different running states; Generate a correlation topology graph by taking the concrete production equipment as a node and taking the optimal time delay order and the state transition synchronization degree as a weight of an edge; Calculate a prediction error of the optimal time delay order and the state transition synchronization degree, determine a weight value according to the prediction error combined with an exponential function, and determine a correlation weight coefficient according to the optimal time delay order and the state transition synchronization degree and the weight value.

[0006] Calculate a synergy efficiency index of a device group based on the correlation weight coefficient, and divide the concrete production equipment into a plurality of dynamic synergy groups according to the synergy efficiency index, including: Add and normalize the correlation weight coefficients of each concrete production equipment and other concrete production equipment to obtain a synergy contribution degree representing each concrete production equipment, and determine a synergy efficiency index according to the correlation weight coefficients of any two concrete production equipment in a group and the synergy contribution degrees of all concrete production equipment; Divide the concrete production equipment whose synergy efficiency index is greater than a preset screening threshold into a group to obtain a candidate grouping scheme, calculate a ratio of an intersection element number to a union element number of a synergy group member set at adjacent two time points, add the group stability index at the last time point to obtain a group stability index at the current time point, and divide the concrete production equipment into a plurality of dynamic synergy groups according to the candidate grouping scheme when the group stability index at continuous multiple time points meets a preset stability threshold.

[0007] Obtain a concrete production task instruction, calculate a production capacity index of each dynamic synergy group based on a device distribution of the dynamic synergy group, including: Obtain a real-time spatial coordinate of each concrete production equipment in the dynamic synergy group, calculate a spatial distribution barycenter of the dynamic synergy group based on the real-time spatial coordinate, and take an average distance from each concrete production equipment to the spatial distribution barycenter as a spatial distribution index; Calculate a matching degree between devices by matching the production parameter data and the production task instruction; According to the matching degree between the devices in the dynamic cooperative group and the spatial distribution index, a cooperative gain coefficient is determined, a basic production capacity of the concrete production device is multiplied by a weighted function value of the cooperative gain coefficient, and a production capacity index of each dynamic cooperative group is obtained.

[0008] Based on the multi-objective evaluation score, a target function of an ant colony algorithm is constructed, and a weight coefficient of a control parameter is taken as a decision variable for iterative optimization. The weight coefficient of the ant colony individual is initialized, and a target function of an ant colony algorithm is constructed based on the multi-objective evaluation score. According to the current path pheromone concentration, a candidate path set is constructed, a dynamic heuristic factor that is adaptively adjusted during the iteration process is calculated based on the current iteration progress, and a selection probability that quantifies the attraction degree of the path is obtained by dividing the product of the current path pheromone concentration multiplied by the dynamic heuristic factor by the sum of the pheromone concentrations of all paths in the candidate path set. Based on the selection probability, path sampling is performed and an evaluation score of the current path is calculated, a parameter step size is calculated according to the current iteration number, and a weight coefficient of the current path is calculated based on the parameter step size, the evaluation score of the current path, and the historical optimal evaluation score. Based on the weight coefficient and a preset basic control quantity, an optimal control instruction sequence is generated.

[0009] The dynamic heuristic factor that is adaptively adjusted during the iteration process is calculated based on the current iteration progress, including: The current iteration progress is substituted into a sinusoidal fluctuation function to generate a fluctuation period factor. According to the optimal target function value obtained in the current iteration and the historical optimal target function value, a convergence state is determined, the convergence state and the fluctuation period factor are combined and substituted into a cosine decreasing function to obtain a stage attenuation parameter, a search intensity parameter is determined according to the target function values of all paths in the current iteration population, and a convergence evaluation value is generated according to the attenuation parameter and the search intensity parameter. The Manhattan distance of the paths in the candidate path set is calculated, a relative distance parameter is obtained based on the distance distribution characteristics, the relative distance parameter and the convergence evaluation value are nonlinearly mapped through an S-shaped function to obtain a diversity adjustment parameter. The convergence evaluation value and the diversity adjustment parameter are combined to generate a dynamic heuristic factor.

[0010] The method further includes: Collect local operation data and train to obtain local optimization parameters, and encrypt the local optimization parameters through a differential privacy encryption algorithm; upload the encrypted local optimization parameters to the cloud for aggregation operation to generate global optimization parameters; and distribute production tasks to each dynamic collaboration group according to the global optimization parameters and the production capacity index.

[0011] In a second aspect of the embodiment of the present application, a remote intelligent control system for a concrete production device is provided, comprising: A first unit is configured to collect production parameter data of the concrete production device, establish an associated topology graph among the concrete production devices according to the production parameter data, and determine an associated weight coefficient; calculate a collaboration efficiency index of a device group based on the associated weight coefficient, and divide the concrete production devices into a plurality of dynamic collaboration groups according to the collaboration efficiency index; A second unit is configured to obtain a concrete production task instruction, calculate a production capacity index of each dynamic collaboration group based on the distribution of devices in the dynamic collaboration groups; A third unit is configured to distribute production tasks to each dynamic collaboration group according to the production capacity index, calculate a multi-objective evaluation score based on the completion, construct a target function of an ant colony algorithm based on the multi-objective evaluation score, use the weight coefficients of the control parameters as decision variables for iterative optimization, select an optimal weight coefficient combination obtained after the iterative optimization, and generate an optimal control instruction sequence.

[0012] In a third aspect of the embodiment of the present application, An electronic device is provided, comprising: A processor; A memory for storing processor-executable instructions; The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0013] In a fourth aspect of the embodiment of the present application, A computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0014] The beneficial effects of the present application are as follows: The present application divides the concrete production devices into a plurality of dynamic collaboration groups and calculates the collaboration efficiency index of the device group based on the associated weight coefficient, thereby realizing intelligent grouping and collaboration control of the concrete production devices and significantly improving the overall collaboration efficiency and resource utilization rate of the production process.

[0015] By constructing the target function based on the ant colony algorithm and iterative optimization, the optimal control instruction sequence can be generated, the concrete production process is more accurate and controllable, the production deviation is reduced, the quality stability and production consistency of the concrete are improved, and the energy consumption and raw material waste are reduced.

[0016] The application constructs a distributed intelligent control system of concrete production based on production parameter data, realizes remote real-time monitoring and intelligent management of production equipment, improves system response speed and data processing efficiency, and enhances the reliability and stability of the production system. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of a remote intelligent control method for concrete production equipment in the embodiment of the application is shown in Figure 2 A flowchart of an associated weight coefficient calculation is shown in DETAILED DESCRIPTION

[0018] To make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0019] The technical scheme of the application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0020] Figure 1 A flowchart of a remote intelligent control method for concrete production equipment in the embodiment of the application is shown in Figure 1 As shown in the figure, the method comprises: Collecting production parameter data of the concrete production equipment; establishing an associated topology graph among the concrete production equipment according to the production parameter data, determining an associated weight coefficient; calculating a coordination efficiency index of a device group based on the associated weight coefficient, and dividing the concrete production equipment into multiple dynamic coordination groups according to the coordination efficiency index; Obtaining a concrete production task instruction, calculating a production capacity index of each dynamic coordination group based on the device distribution of the dynamic coordination group; According to the production capacity index, production tasks are allocated to each dynamic coordination group, a multi-objective evaluation score is calculated based on the completion, a target function of an ant colony algorithm is constructed based on the multi-objective evaluation score, and the weight coefficients of control parameters are used as decision variables for iterative optimization, an optimal weight coefficient combination obtained after iterative optimization is selected, and an optimal control instruction sequence is generated.

[0021] Figure 2 A flowchart for calculating the correlation weight coefficient is shown. In an optional embodiment, an associated topological graph between the concrete production equipment is established according to the production parameter data, and the correlation weight coefficient is determined, including: According to the production parameter data, mutual information entropy values under different time delay orders are calculated, the mutual information entropy values are combined to form a multi-scale mutual information entropy sequence, and a local maximum point in the multi-scale mutual information entropy sequence is extracted as an optimal time delay order; The state transition frequencies of the concrete production equipment in different operating states are counted, and the state transition synchronization degree of the concrete production equipment is determined according to the difference of the elements in the state transition probability matrix corresponding to the state transition frequencies; The concrete production equipment is taken as a node, and the optimal time delay order and the state transition synchronization degree are taken as the weight of the edge to generate an associated topological graph; The prediction error of the optimal time delay order and the state transition synchronization degree is calculated, the weight value is determined according to the prediction error combined with an exponential function, and the correlation weight coefficient is determined according to the optimal time delay order and the state transition synchronization degree, and the weight value.

[0022] In a concrete production line, production parameter data of multiple equipment are collected, including the operating state, temperature, speed, material flow and other parameters of the mixer, metering system, conveyor belt and other equipment. These parameter data are recorded in time series form, the sampling frequency is one per second, continuous sampling is performed for 72 hours, and a total of 259200 data points are generated.

[0023] For calculating the mutual information entropy value at different time delay orders, the parameter data of each device is first standardized to make the data mean value 0 and the variance 1. Then, the time delay order value range is set to 0 to 100, i.e. the delay is 0 to 100 seconds. For each delay order τ, the mutual information entropy value between device 1 and device 2 is calculated. The specific operation is to pair the parameter data sequence of device 1 with the parameter data sequence of device 2 delayed by τ time, to count their joint distribution probability and marginal distribution probability, and then to calculate the mutual information entropy value. Taking the mixer and the metering system as an example, when the delay order τ = 0, the mutual information entropy value is 0.15; when τ = 5, the mutual information entropy value rises to 0.42; when τ = 27, it reaches the peak value 0.87; when τ = 60, it drops to 0.23. Arrange these mutual information entropy values according to the time delay order to form a multi-scale mutual information entropy sequence [0.15, 0.21, 0.33, 0.38, 0.42, …, 0.87, …, 0.23, 0.19]. Through the local maximum value detection algorithm, the peak points in the sequence are identified, and in this example, τ = 27 is the local maximum value point, so the optimal time delay order between the mixer and the metering system is determined to be 27 seconds.

[0024] The running state category division of the production parameter data is completed based on clustering analysis. The parameter data of each device is feature extracted, and the mean value, standard deviation, peak factor, skewness and kurtosis are selected as features, and the K-means clustering algorithm is used to divide the running state into 4 categories: normal operation, low load, high load and abnormal state. For the mixer, state 1 indicates that the mixing speed is 60-70 revolutions per minute and the material is mixed evenly; state 2 indicates that the speed is 45-60 revolutions per minute and the material is not mixed well; state 3 indicates that the speed is 70-85 revolutions per minute and the material is over-mixed; and state 4 indicates that the speed is abnormally fluctuating or in a shutdown state.

[0025] Based on the divided running state categories, the state transition frequency of the concrete production equipment is counted. A state transition matrix is constructed, and the matrix elements represent the number of times of transition from state i to state j. Taking the mixer as an example, the transition frequency from state 1 to state 1 is 8560 times, from state 1 to state 2 is 1240 times, from state 1 to state 3 is 320 times, and from state 1 to state 4 is 80 times. The transition matrix is normalized to obtain a state transition probability matrix, such as the transition probability from state 1 to state 1 is 0.84, from state 1 to state 2 is 0.12, from state 1 to state 3 is 0.03, and from state 1 to state 4 is 0.01.

[0026] The difference between the elements of the state transition probability matrix of different devices is calculated. For the mixer and the metering system, the difference between the state 1 to state 1 transition probability is |0.84-0.81|=0.03, the difference between the state 1 to state 2 transition probability is |0.12-0.13|=0.01, and so on. The average of these differences is 0.025 for the mixer and the metering system.

[0027] The concrete production equipment is taken as a node to construct the correlation topology graph. Five main devices, including the mixer, the metering system, the conveyor belt, the cement warehouse, and the sandstone warehouse, are taken as nodes, forming a total of 10 edges of an undirected graph. Each edge has two weights: the optimal time delay order and the state transition synchronization degree. For example, the edge weight between the mixer and the metering system is (27, 0.86), the edge weight between the mixer and the conveyor belt is (15, 0.72), and the edge weight between the metering system and the conveyor belt is (8, 0.65).

[0028] In order to integrate the two weights, the prediction errors of the optimal time delay order and the state transition synchronization degree are calculated. Through ten-fold cross-validation, the time delay prediction mean square error of the mixer and the metering system is 0.043, and the state transition synchronization degree prediction mean square error is 0.028. The prediction errors are substituted into the exponential function e -误差 , and the time sequence correlation weight coefficient is e -0.043 =0.958, and the state correlation weight coefficient is e -0.028 =0.972.

[0029] Finally, the correlation weight coefficient is calculated by weighted summation. The correlation weight coefficient of the mixer and the metering system is 27×0.958+0.86×0.972=26.71. Similarly, the correlation weight coefficients of other device pairs are calculated to generate a complete correlation topology graph, thereby realizing the quantitative characterization of the relationship between the concrete production equipment and providing data support for production scheduling and fault diagnosis.

[0030] In an alternative embodiment, a coordination efficiency indicator of a device group is calculated based on the correlation weight coefficient, and the concrete production equipment is divided into multiple dynamic coordination groups according to the coordination efficiency indicator, comprising: The correlation weight coefficients of each concrete production equipment and other concrete production equipment are added and normalized to obtain the coordination contribution degree of each concrete production equipment; the coordination efficiency indicator is determined according to the correlation weight coefficients of any two concrete production equipment in the group and the coordination contribution degrees of all concrete production equipment; The concrete production equipment with the synergistic efficiency index greater than the preset screening threshold is divided into a group to obtain a candidate grouping scheme, a ratio of a number of intersection elements of the synergistic group member set at two adjacent time points in the candidate grouping scheme to a number of union elements is calculated, and the group stability index at the previous time point is added to obtain the group stability index at the current time point; when the group stability index at continuous multiple time points all satisfies a preset stability threshold, the concrete production equipment is divided into multiple dynamic synergistic groups in accordance with the candidate grouping scheme.

[0031] In an actual application scenario, first, the running state data of each device in a concrete production system is acquired. Taking a certain concrete production plant as an example, the plant has 8 main production devices, which are 2 mixers (labeled as M1 and M2), 3 delivery pumps (labeled as P1, P2, and P3), and 3 loaders (labeled as L1, L2, and L3). The running state data of the devices is collected every 10 minutes, including the key parameters such as the on-time, off-time, running power, and material processing amount of the devices.

[0032] Based on the collected device running state data, the correlation weight coefficient between the devices is calculated. The correlation weight coefficient reflects the strength of the synergistic operation relationship between two devices. In this embodiment, the correlation weight coefficient is determined by calculating the weighted values in three dimensions of the device running time overlap degree, material flow relationship, and energy consumption correlation. For example, when the running time of the mixer M1 and the delivery pump P1 is highly overlapped, and the material flow is close, the correlation weight coefficient of the two is high, and the specific value is 0.82; while the synergistic relationship between M1 and L3 is weak, and the correlation weight coefficient is only 0.21.

[0033] Next, the synergistic contribution degree of each device is calculated based on the correlation weight coefficient. For each concrete production device, the correlation weight coefficients with all other devices are added and then normalized to obtain the synergistic contribution degree of the device. For example, the correlation weight coefficients of M1 with the other 7 devices (0.76, 0.82, 0.45, 0.35, 0.21, 0.33, and 0.28) are added to obtain 3.2, and the normalized synergistic contribution degree of M1 is 0.18. Similarly, the synergistic contribution degrees of other devices are calculated: M2 is 0.16, P1 is 0.21, P2 is 0.19, P3 is 0.11, L1 is 0.06, L2 is 0.05, and L3 is 0.04.

[0034] Based on the device-based synergy contribution degree and the correlation weight coefficient, the synergy efficiency index of the device group is calculated. After selecting the device combination, the correlation weight coefficient of any two devices in the group is multiplied by the synergy contribution degree of the corresponding device and accumulated. For example, considering the device combination {M1, M2, P1, P2}, the correlation weight coefficient 0.76 of M1 and M2 is multiplied by the product of the synergy contribution degree 0.18 of M1 and the synergy contribution degree 0.16 of M2, and the product is 0.022; the weighted values of all device pairs in the combination are calculated in the same way and accumulated, and the original value of the synergy efficiency of the combination is 0.153. Divide this value by the maximum possible value 0.172 of the sum of the squares of the synergy contribution degrees of all devices in the group, and finally obtain the synergy efficiency index as 0.89.

[0035] According to the calculated synergy efficiency index, the device combination with high efficient synergy is screened out. Set the preset screening threshold value as 0.75, and for the device combination with a synergy efficiency index greater than 0.75, it is taken as a candidate grouping scheme. In this example, the synergy efficiency index of the device combination {M1, M2, P1, P2} is 0.89, which is greater than the threshold value 0.75, so it is selected as the candidate grouping scheme one; the synergy efficiency index of the device combination {P3, L1, L2} is 0.81, which is also selected as the candidate grouping scheme two; and the device combination {L3} is taken as a single group, forming the candidate grouping scheme three.

[0036] In order to ensure the stability of the grouping scheme, the group stability index needs to be calculated. At consecutive multiple time points, the ratio of the number of intersection elements to the number of union elements of the synergy group member set at adjacent two time points is calculated. For example, at time t and t+1, the device combination in the candidate grouping scheme one is {M1, M2, P1, P2} and {M1, M2, P1, P2, L1} respectively, the number of intersection elements is 4, the number of union elements is 5, and the ratio is 0.8. Assuming that the group stability index at the last time is 0.85, the group stability index at the current time is 0.8+0.85=1.65.

[0037] Set the preset stability threshold value as 1.5, when the group stability index at consecutive 5 time points is greater than 1.5, it is considered that the grouping scheme is stable and reliable. In this example, after continuous monitoring, the group stability index of the candidate grouping scheme one exceeds 1.5 at consecutive 6 time points, which are 1.65, 1.72, 1.68, 1.79, 1.82 and 1.76 respectively. Therefore, the concrete production devices are formally divided into three dynamic synergy groups: the first group includes devices M1, M2, P1, P2, mainly responsible for the mixing and main conveying tasks of concrete; the second group includes devices P3, L1, L2, mainly responsible for auxiliary conveying and material loading; and the third group includes device L3, responsible for independent material transportation task.

[0038] Through the above dynamic coordination group division method, the concrete production system can realize efficient coordinated operation of the equipment. For example, the mixers M1 and M2 in the first coordination group cooperate closely with the delivery pumps P1 and P2, the material flow is smooth, the waiting time is reduced, and the production efficiency is improved by about 23%; at the same time, the energy consumption is reduced by about 17%, and the equipment failure rate is reduced by about 12%. The method can dynamically adjust the coordination group division according to the actual running state of the equipment, adapt to the change of production demand, and ensure the efficient and stable operation of the concrete production system.

[0039] In an alternative embodiment, a concrete production task instruction is obtained, and based on the distribution of the equipment of the dynamic coordination group, the production capacity index of each dynamic coordination group is calculated, including: The real-time spatial coordinates of each concrete production equipment in the dynamic coordination group are obtained, and the spatial distribution center of gravity of the dynamic coordination group is calculated based on the real-time spatial coordinates, and the average distance from each concrete production equipment to the spatial distribution center of gravity is taken as the spatial distribution index; The matching degree between the production parameter data and the production task instruction is calculated to obtain the matching degree between the equipment; The coordination gain coefficient is determined according to the matching degree between the equipment in the dynamic coordination group and the spatial distribution index, and the basic production capacity of the concrete production equipment is multiplied by the weighted function value of the coordination gain coefficient to obtain the production capacity index of each dynamic coordination group.

[0040] The concrete production task instruction is received from the production scheduling center, which includes the type, quantity, delivery time and other key information of the required concrete. At the same time, a plurality of dynamic coordination groups have been pre-constructed, each coordination group being composed of a plurality of concrete production equipment with communication capability, such as mixing station, loader, transport vehicle, etc.

[0041] In order to calculate the production capacity index of each dynamic coordination group, the real-time spatial coordinates of each concrete production equipment in the dynamic coordination group are first obtained. These coordinates can be obtained through the GPS module or base station positioning system on the equipment, with an accuracy of meters. For example, in a certain dynamic coordination group, the coordinates of mixer A are (120, 150, 0), the coordinates of cement bin B are (125, 160, 5), the coordinates of loader C are (110, 155, 0), and the coordinates of transport vehicle D are (130, 145, 0), all in meters.

[0042] The spatial distribution center of gravity of the dynamic collaborative group is calculated according to the acquired real-time spatial coordinates. The calculation of the spatial distribution center of gravity is obtained by averaging the coordinate values of each device. Taking the above four devices as an example, the spatial distribution center of gravity coordinates are ((120+125+110+130) / 4, (150+160+155+145) / 4, (0+5+0+0) / 4), i.e. (121.25, 152.5, 1.25).

[0043] Then the distance of each device to the spatial distribution center of gravity is calculated. Using the three-dimensional space distance calculation formula, the distance of device A to the center of gravity is 12.74 meters, the distance of device B to the center of gravity is 15.36 meters, the distance of device C to the center of gravity is 13.69 meters, and the distance of device D to the center of gravity is 14.58 meters. Taking the average of these distance values, the spatial distribution index is 14.09 meters, which reflects the compactness of the device distribution.

[0044] It is also necessary to calculate the matching degree between devices. The matching degree between devices is an index that measures the degree of fit between device performance parameters and production task requirements. The production parameter data of each device is obtained from the device management database, including device model, production efficiency, material adaptability, etc. These parameters are compared and calculated with the requirements in the production task instructions.

[0045] Taking the production of C40 concrete as an example, the adaptability of mixer A is 0.95, the adaptability of cement bin B is 0.90, the adaptability of loader C is 0.85, and the adaptability of transport vehicle D is 0.92. Considering the compatibility of cooperation between devices, the matching degree of device A and B is 0.93, the matching degree of device A and C is 0.88, the matching degree of device A and D is 0.90, the matching degree of device B and C is 0.86, the matching degree of device B and D is 0.89, and the matching degree of device C and D is 0.87.

[0046] According to the spatial distribution index calculated above, a distance attenuation function is constructed. This function reflects the influence of the physical distance between devices on the cooperation efficiency, and generally the farther the distance, the lower the cooperation efficiency. In this embodiment, the distance attenuation function adopts an exponential decay form, where the attenuation coefficient is determined according to the specific production scene. For the spatial distribution index of 14.09 meters, the calculated distance attenuation value is 0.85.

[0047] The matching degree between devices in the dynamic collaborative group is multiplied by the calculation result of the distance attenuation function and accumulated to obtain the collaborative gain coefficient. The specific calculation process is to multiply the matching degree of each pair of devices by the corresponding distance attenuation value, then sum and normalize. In this example, the values obtained by multiplying the matching degrees of the six pairs of devices by 0.85 are 0.79, 0.75, 0.77, 0.73, 0.76, and 0.74, respectively. The cumulative value is 4.54, and the normalized processing obtains the collaborative gain coefficient 1.22.

[0048] Finally, the basic productivity data of each device is obtained. The basic productivity refers to the production capacity of the device under standard working conditions, for example, the basic productivity of mixer A is 120 cubic meters / hour, the feeding capacity of cement warehouse B is converted to 130 cubic meters / hour, the material handling capacity of loader C is converted to 100 cubic meters / hour, and the transportation capacity of transport vehicle D is converted to 110 cubic meters / hour. The basic productivity of the dynamic synergy group depends on the device with the lowest productivity, which is 100 cubic meters / hour in this example.

[0049] The production capacity index of the dynamic synergy group is obtained by multiplying the basic productivity by the weighted function value of the synergy gain coefficient. The weighted function is designed as the basic value 1 plus the synergy gain coefficient multiplied by a weight factor 0.2, and the calculation result is 1.244. The basic productivity 100 cubic meters / hour is multiplied by 1.244, and the final production capacity index of the dynamic synergy group is 124.4 cubic meters / hour.

[0050] The above calculation process is repeated for all dynamic synergy groups to obtain the production capacity index of each synergy group. These indexes will be important basis for subsequent resource scheduling and task allocation, realizing intelligent and accurate management of the concrete production process and improving overall production efficiency.

[0051] In an optional embodiment, a target function of an ant colony algorithm is constructed based on the multi-objective evaluation score, and the weight coefficients of the control parameters are taken as decision variables for iterative optimization. An optimal weight coefficient combination obtained after iterative optimization is selected to generate an optimal control instruction sequence, which includes: The weight coefficients of the ant colony individuals are initialized, and a target function of an ant colony algorithm is constructed based on the multi-objective evaluation score. The local optimal solution and the global optimal solution obtained by the ant colony individuals in the current iteration are recorded based on the target function. The local optimal solution and the global optimal solution are added after being multiplied by a preset reinforcement coefficient, to obtain the current path pheromone concentration; A candidate path set is constructed according to the current path pheromone concentration. A dynamic heuristic factor that is adaptively adjusted during the iteration process is calculated based on the current iteration progress. A selection probability that quantifies the attraction degree of the path is obtained by dividing the product of the current path pheromone concentration multiplied by the dynamic heuristic factor by the sum of the pheromone concentrations of all paths in the candidate path set. Path sampling is performed based on the selection probability, and an evaluation score of the current path is calculated. A parameter step size is calculated according to the current iteration number. The weight coefficients of the current path are calculated based on the parameter step size, the evaluation score of the current path, and the historical optimal evaluation score. An optimal control instruction sequence is generated based on the weight coefficients and a preset basic control quantity.

[0052] The weight coefficient of the ant colony individual is initialized as 0.5, which is used to adjust the influence degree of each control parameter on the concrete production process. The objective function of the ant colony algorithm is constructed based on the multi-objective evaluation score, which includes four aspects of concrete quality index, production efficiency index, energy consumption index and equipment wear index. The concrete quality index is scored based on the compressive strength, workability and uniformity, with a score range of 0-100; the production efficiency index is scored based on the unit time yield and production continuity, with a score range of 0-100; the energy consumption index is scored based on the unit yield of power, water and fuel consumption, with a score range of 0-100; the equipment wear index is scored based on the usage state and expected life of the key components, with a score range of 0-100.

[0053] The objective function is designed in the form of weighted sum of the four evaluation indexes, that is, the objective function value is equal to the quality index score multiplied by the weight coefficient w1 plus the production efficiency index score multiplied by the weight coefficient w2 plus the energy consumption index score multiplied by the weight coefficient w3 plus the equipment wear index score multiplied by the weight coefficient w4. Initially, w1=0.3, w2=0.3, w3=0.2, w4=0.2, indicating that more attention is paid to concrete quality and production efficiency. The local optimal solution obtained by the ant colony individual in the current iteration and the global optimal solution are recorded based on the objective function, the local optimal solution refers to the best control parameter combination found by a single ant colony individual in its exploration path and its corresponding objective function value, and the global optimal solution refers to the best control parameter combination found by all ant colony individuals and its corresponding objective function value.

[0054] The local optimal solution is multiplied by a preset local reinforcement coefficient of 0.4, the global optimal solution is multiplied by a preset global reinforcement coefficient of 0.6, and then added to obtain the pheromone concentration of the current path. For example, when the objective function value of the local optimal solution is 85 and the objective function value of the global optimal solution is 90, the pheromone concentration of the current path is 85 multiplied by 0.4 plus 90 multiplied by 0.6, which is calculated as 88. The candidate path set is constructed based on the current path pheromone concentration, which contains multiple possible control parameter combination schemes. In concrete production, the control parameters include cement dosage, sand stone ratio, admixture dosage, mixing time and mixing speed, etc.

[0055] The dynamic heuristic factor is calculated based on the current iteration progress, and the value of the dynamic heuristic factor is equal to 1 minus the current iteration number divided by the maximum iteration number multiplied by 0.7 plus 0.3. For example, when the current iteration number is 50 and the maximum iteration number is 100, the dynamic heuristic factor is 1 minus 50 divided by 100 multiplied by 0.7 plus 0.3, which is calculated as 0.65. The current path pheromone concentration is multiplied by the dynamic heuristic factor and then divided by the sum of the product of the pheromone concentrations of all paths in the candidate path set, to obtain the selection probability of the quantified path attraction degree.

[0056] Taking the concrete mixing time control as an example, assuming that there are three candidate mixing time settings: 60 seconds, 90 seconds and 120 seconds, the corresponding pheromone concentrations are 70, 88 and 75 respectively, and the dynamic heuristic factor is 0.65. Then the selection probability of the 60-second mixing time is 70 multiplied by 0.65 divided by (70 multiplied by 0.65 plus 88 multiplied by 0.65 plus 75 multiplied by 0.65), which is calculated to be 0.3; the selection probability of the 90-second mixing time is 88 multiplied by 0.65 divided by (70 multiplied by 0.65 plus 88 multiplied by 0.65 plus 75 multiplied by 0.65), which is calculated to be 0.38; and the selection probability of the 120-second mixing time is 75 multiplied by 0.65 divided by (70 multiplied by 0.65 plus 88 multiplied by 0.65 plus 75 multiplied by 0.65), which is calculated to be 0.32.

[0057] The path sampling is performed based on the selection probability and the evaluation score of the current path is calculated. The path sampling adopts the roulette selection method to generate a random number between 0 and 1, if the random number is less than 0.3, the 60-second mixing time is selected; if the random number is greater than or equal to 0.3 and less than 0.68 (0.3+0.38), the 90-second mixing time is selected; if the random number is greater than or equal to 0.68 and less than or equal to 1, the 120-second mixing time is selected. The evaluation score of the selected mixing time setting is calculated in combination with other control parameters through actual production or simulation.

[0058] The parameter step is calculated according to the current iteration number, which is equal to 0.15 multiplied by (1 minus the current iteration number divided by the maximum iteration number). For example, when the current iteration number is 50 and the maximum iteration number is 100, the parameter step is 0.15 multiplied by (1 minus 50 divided by 100), which is calculated to be 0.075. The weight coefficient of the current path is obtained by multiplying the parameter step by the difference between the evaluation score of the current path and the historical optimal evaluation score. Assuming that the evaluation score of the current path is 82 and the historical optimal evaluation score is 78, the weight coefficient of the current path is 0.075 multiplied by (82 minus 78), which is calculated to be 0.3.

[0059] The optimal weight coefficient after smoothing is obtained by weighted averaging the weight coefficient of the current path and the weight coefficient at the last time. The weighted averaging adopts 0.7 as the weight of the current weight coefficient and 0.3 as the weight of the weight coefficient at the last time. Assuming that the weight coefficient at the last time is 0.4 and the weight coefficient of the current path is 0.3, the optimal weight coefficient after smoothing is 0.3 multiplied by 0.7 plus 0.4 multiplied by 0.3, which is calculated to be 0.33. The optimal control instruction sequence is generated based on the optimal weight coefficient and the preset basic control quantity.

[0060] The preset basic control amount of the concrete mixer includes: a cement amount reference value 400 kg, a sandstone ratio reference value 1:2.5, an additive amount reference value 5 kg, a mixing time reference value 90 seconds, and a mixing speed reference value 30 revolutions / minute. According to the optimal weight coefficient 0.33, the generated optimal control instruction sequence is: the cement amount is set to 413.2 kg (400+400*0.033), the sandstone ratio is set to 1:2.42 (2.5-2.5*0.033), the additive amount is set to 5.17 kg (5+5*0.033), the mixing time is set to 92.97 seconds (90+90*0.033), and the mixing speed is set to 29.01 revolutions / minute (30-30*0.033).

[0061] Through the application of the method in actual concrete production, the compressive strength of the concrete is increased by 12%, the production continuity is increased by 18%, the energy consumption per unit of output is reduced by 15%, and the service life of key components is prolonged by 20%, thereby realizing the overall optimization of the concrete production process.

[0062] In an alternative embodiment, a dynamic heuristic factor is calculated based on the current iteration progress and is self-adaptively adjusted during the iteration process, including: The current iteration progress is substituted into a sinusoidal fluctuation function to generate a fluctuation period factor; A convergence state is determined according to the optimal objective function value obtained in the current iteration and the historical optimal objective function value, the convergence state and the fluctuation period factor are substituted into a cosine decreasing function to obtain a stage attenuation parameter, a search intensity parameter is determined according to the objective function values of all paths in the current iteration population, and a convergence evaluation value is generated according to the attenuation parameter and the search intensity parameter; The Manhattan distance of the paths in the candidate path set is calculated, a relative distance parameter is obtained based on the distance distribution characteristics, the relative distance parameter and the convergence evaluation value are nonlinearly mapped through an S-shaped function to obtain a diversity adjustment parameter; The convergence evaluation value and the diversity adjustment parameter are combined to generate a dynamic heuristic factor.

[0063] The dynamic heuristic factor generation method first substitutes the current iteration progress into the sinusoidal fluctuation function to generate a fluctuation period factor. Specifically, if the maximum number of iterations is set to 1000 and the current number of iterations is 325, the current iteration progress is calculated as 325 divided by 1000, resulting in 0.325. After substituting this progress value into the sinusoidal fluctuation function, i.e., multiplying 0.325 by 2 by pi, and taking the sine value, the fluctuation period factor value is obtained as 0.382. In practical applications, in order to enhance the controllability of periodic changes, a fluctuation frequency parameter can be introduced. For example, when the fluctuation frequency parameter is set to 2.5, after multiplying 0.325 by 2 by pi by 2.5 and taking the sine value, the fluctuation period factor becomes 0.195, showing more frequent periodic changes, which helps the algorithm maintain appropriate perturbability during the search process.

[0064] Next, the convergence state is determined according to the optimal objective function value obtained in the current iteration and the historical optimal objective function value. In the concrete production equipment collaborative optimization problem, the objective function value reflects the comprehensive evaluation of production efficiency, energy consumption, and equipment stability. For example, the optimal objective function value of the current 325th iteration is 78.35, while the historical optimal objective function value is 78.42, and the relative difference between the two is (78.42-78.35) / 78.42 equal to 0.0009, indicating that the algorithm is in a state close to convergence. Substituting the convergence state value 0.0009 and the aforementioned fluctuation period factor 0.382 into the cosine decreasing function, i.e., multiplying 0.5 by (1+cosine value(0.0009 times pi)) by (1-0.382), the stage attenuation parameter is obtained as 0.613.

[0065] The search intensity parameter is determined according to the objective function values of all paths in the current iteration population. In the 325th iteration, the population contains 50 candidate solutions, each representing a control instruction sequence for a set of concrete production equipment. The objective function values of these candidate solutions are distributed as: 75.21, 76.84, 77.05, 77.92, 78.35, etc. The standard deviation of all 50 values is calculated to be 2.86. By substituting the standard deviation 2.86 into the mapping function, i.e., 1 minus 2.86 divided by (2.86 plus 10), the search intensity parameter is obtained as 0.75.

[0066] The aforementioned attenuation parameter and search intensity parameter are combined linearly to generate a convergence evaluation value. Assigning the attenuation parameter 0.613 a weight of 0.7 and the search intensity parameter 0.75 a weight of 0.3, calculating 0.613 times 0.7 plus 0.75 times 0.3, the convergence evaluation value is obtained as 0.654.

[0067] The Manhattan distance of the paths in the candidate path set is calculated, and the relative distance parameter is obtained based on the distance distribution characteristics. In the population of the 325th iteration, two representative paths are selected: path A is represented as [5, 8, 2, 9, 3, 7, 1, 6, 4, 8, 5, 2, 7, 9, 3, 1, 5, 8, 2, 4], and path B is represented as [5, 7, 3, 8, 2, 6, 2, 7, 5, 7, 4, 3, 8, 9, 2, 1, 6, 7, 3, 5]. Here, each number represents the control instruction of the corresponding concrete production equipment at the corresponding position, ranging from 0 to 9. The Manhattan distance of the two paths is calculated: the first position is the same, 0; the difference between the second position is |8-7|=1; the difference between the third position is |2-3|=1; and so on. The total Manhattan distance is obtained by adding the differences of all positions, which is 24. The Manhattan distance of all 50 candidate paths is calculated pairwise, and a total of 1225 distance values are obtained. The mean of these distances is 18.7, and the standard deviation is 5.2. The mean and standard deviation are substituted into the calculation formula, i.e., 18.7 divided by (50 times 20 times 0.5) multiplied by (1 minus 5.2 divided by 50), to obtain the relative distance parameter as 0.427.

[0068] The relative distance parameter and the convergence evaluation value are nonlinearly mapped through an S-shaped function to obtain the diversity adjustment parameter. The relative distance parameter 0.427 and the convergence evaluation value 0.654 are substituted into the S-shaped function, i.e., 1 divided by (1 plus the exponential function value (negative (0.427 minus 0.5) times 10 times (1 minus 0.654))), to obtain the diversity adjustment parameter as 0.583.

[0069] The dynamic heuristic factor is generated in combination with the convergence evaluation value and the diversity adjustment parameter. In the ant colony algorithm, the convergence evaluation value 0.654 is mapped to the pheromone importance factor, and the diversity adjustment parameter 0.583 is mapped to the heuristic information importance factor. Specifically, the pheromone importance factor is set to the reference value 1.5 times the convergence evaluation value 0.654, obtaining 0.981; and the heuristic information importance factor is set to the reference value 2.0 times the diversity adjustment parameter 0.583, obtaining 1.166.

[0070] In the example of collaborative optimization control of concrete production equipment, the dynamic heuristic factor is applied to the path selection probability calculation of the 325th iteration. For a production system containing 30 equipment, the equipment numbers are E1 to E30, and the control instruction of each equipment can be selected from 0-9. For example, the control instruction "3" of E5 equipment means running at medium speed, and the instruction "7" means running at higher speed. In the 325th iteration, for the path selection of ant k from equipment E8 to equipment E15, if there are control instruction options 5, 6, and 7, their pheromone concentrations are 0.28, 0.35, and 0.22 respectively, and the heuristic information values are 0.15, 0.12, and 0.20 respectively. Using the dynamically generated heuristic factors 0.981 and 1.166, the selection probabilities are calculated: the selection probability of control instruction 5 is (0.28 raised to the power of 0.981 multiplied by 0.15 raised to the power of 1.166) divided by the sum of similar calculations of all options, which is 0.312; the selection probability of control instruction 6 is (0.35 raised to the power of 0.981 multiplied by 0.12 raised to the power of 1.166) divided by the same denominator, which is 0.327; the selection probability of control instruction 7 is (0.22 raised to the power of 0.981 multiplied by 0.20 raised to the power of 1.166) divided by the same denominator, which is 0.361. Based on these probabilities, ant k has a 36.1% probability of selecting control instruction 7 to control equipment E15.

[0071] By applying the dynamic heuristic factor through continuous iterations, the algorithm exhibits different search characteristics at different stages. In the early iterations (such as the 100th), due to the lower convergence evaluation value (about 0.321) and the higher diversity adjustment parameter (about 0.782), the pheromone importance factor is smaller and the heuristic information importance factor is larger, so the algorithm tends to explore new solution space. In the later iterations (such as the 800th), the convergence evaluation value increases to about 0.875 and the diversity adjustment parameter decreases to about 0.426, resulting in an increase in the pheromone importance factor and a decrease in the heuristic information importance factor, so the algorithm focuses more on fine search in the discovered high-quality solution area.

[0072] The experimental data show that the optimization method using dynamic heuristic factor has significantly better performance than the method using fixed heuristic factor under the same number of iterations. At the 450th iteration, the optimal control instruction sequence found by the dynamic heuristic factor method is [7, 3, 5, 8, 2, 6, 9, 4, 7, 3, 8, 5, 6, 2, 7, 4, 9, 5, 3, 7, 8, 4, 6, 2, 9, 3, 5, 7, 4, 8], and the corresponding objective function value is 83.67. While the optimal value of the method using fixed heuristic factor at the 450th iteration is only 76.24, and it does not reach a close optimization effect (the objective function value is 83.21) until the 700th iteration. The control instruction sequence found by the dynamic heuristic factor method enables the concrete mixing equipment E7, E8, and E9 and the conveying equipment E15, E16, and E17 to form a high-efficiency collaborative working group, which improves the collaborative efficiency of these devices from the baseline value of 0.72 to 0.89, with an increase of 23.6%.

[0073] In an alternative embodiment, the method further comprises: Collecting local running data and training to obtain local optimization parameters, encrypting the local optimization parameters by a differential privacy encryption algorithm; uploading the encrypted local optimization parameters to the cloud for aggregation operation to generate global optimization parameters; and assigning production tasks to each dynamic collaborative group according to the global optimization parameters and the production capacity indicators.

[0074] The local running data collected include three categories of data: concrete mixing parameters, equipment running parameters, and quality control parameters. The mixing parameters include cement dosage, coarse and fine aggregate ratio, admixture type and dosage, and water-cement ratio; the equipment running parameters include mixer speed, motor load rate, hydraulic system pressure, and mixing time; and the quality control parameters include concrete slump, concrete temperature, air content, and initial setting time. The data collection frequency is set to 6 times per minute to ensure the capture of key change information in the production process.

[0075] The collected data is used for model training after local preprocessing. The preprocessing steps include data cleaning, feature extraction, and data standardization. Data cleaning removes noise by moving median filtering method, with a window size of 7 to effectively remove transient outliers; feature extraction calculates key production indicators such as energy efficiency, capacity utilization rate, and quality consistency score; and data standardization uses the maximum and minimum value normalization method to map each parameter to the 0-1 interval. The preprocessed data set is used to train the local optimization model, which uses the gradient boosting decision tree algorithm, and the training target is to maximize the concrete production comprehensive performance index.

[0076] The comprehensive performance index is composed of quality score, energy efficiency score and production capacity score, with weights of 0.5, 0.3 and 0.2 respectively. The model training adopts a five-fold cross-validation method, with the number of iterations set to 200, the learning rate set to 0.08, and the maximum tree depth set to 6. The feature importance, tree structure and split threshold information extracted from the trained model are used as local optimization parameters. For example, the weight of cement quality on concrete strength is 0.42, the weight of mixing time on concrete uniformity is 0.38, and the weight of admixture dosage on slump is 0.35.

[0077] The local optimization parameters are encrypted by differential privacy encryption algorithm to protect the privacy of production data while realizing safe sharing. Differential privacy encryption first determines the privacy protection budget ε, which is 1.5 in this embodiment. A larger ε value provides lower privacy protection but maintains higher data availability, which is suitable for concrete production scenarios that require high data accuracy. Noise is added to each local optimization parameter, and the noise conforms to the Laplace distribution with a scale parameter equal to the parameter sensitivity divided by the privacy budget.

[0078] The parameter sensitivity is determined through historical data analysis. The sensitivity of cement quality weight is set to 0.08, the sensitivity of mixing time weight is set to 0.05, and the sensitivity of admixture dosage weight is set to 0.06. After encryption, the cement quality weight becomes 0.42 plus the random noise conforming to the Laplace distribution with a mean of 0 and a scale of 0.08 / 1.5; the mixing time weight becomes 0.38 plus the random noise conforming to the Laplace distribution with a mean of 0 and a scale of 0.05 / 1.5; and the admixture dosage weight becomes 0.35 plus the random noise conforming to the Laplace distribution with a mean of 0 and a scale of 0.06 / 1.5.

[0079] The encryption effect evaluation adopts the privacy leakage risk test method, simulating an attacker trying to reverse the original parameters from the encrypted parameters. In 1,000 simulated attacks, the attacker's success rate was only 3.8%, far below the 10% safety threshold, proving that the encryption scheme effectively protects parameter privacy. The encrypted local optimization parameters are uploaded to the cloud server through a secure transmission channel, using the TLS 1.3 protocol and a two-way authentication mechanism to ensure data transmission security.

[0080] The cloud server is deployed in a secure and reliable data center and is configured with a high-performance computing cluster for parameter aggregation operations. The cloud server receives encrypted parameters and uses the federated averaging algorithm for secure aggregation to generate global optimization parameters. The federated averaging algorithm considers the data quality and quantity of each node and assigns a reasonable weight to each node. Data quality is evaluated through consistency testing and abnormality rate, and data quantity is determined by the number of valid samples.

[0081] Taking five concrete production bases in the region as an example, the cement quality influence weight uploaded by each base (after encryption) is 0.43, 0.40, 0.45, 0.41, and 0.39 respectively, and the corresponding weight distribution is 0.22, 0.18, 0.25, 0.20, and 0.15. The global cement quality influence weight after aggregation is 0.43 multiplied by 0.22 plus 0.40 multiplied by 0.18 plus 0.45 multiplied by 0.25 plus 0.41 multiplied by 0.20 plus 0.39 multiplied by 0.15, which is calculated to be 0.424. The aggregation process utilizes the characteristic that the noise expectation is zero, and as the number of participating nodes increases, the encrypted noise cancels out, improving the accuracy of the aggregation result.

[0082] After generating the global optimization parameters, the production capacity indicators of each production base are combined to allocate production tasks to each dynamic collaboration group. The production capacity indicators include equipment capacity index, material supply sufficiency, human resource status, and transportation capacity score. The equipment capacity index is calculated based on the type of mixing equipment, rated output, and actual operating efficiency, with a value range of 0-100; the material supply sufficiency is calculated based on the ratio of main raw material inventory to consumption rate, with a value range of 0-100; the human resource status is evaluated based on the number of operators, skill level, and shift arrangement, with a value range of 0-100; the transportation capacity score is evaluated based on the number of transportation vehicles, average transport distance, and road conditions, with a value range of 0-100.

[0083] Dynamic collaboration groups are formed based on geographical location and production capacity complementarity, usually consisting of 3-5 concrete production bases. The system allocates production tasks to each collaboration group using a multi-objective optimization algorithm based on global optimization parameters and production capacity indicators. The task allocation objectives include minimizing production cost, minimizing delivery time, and maximizing quality stability, taking into account the priority of customer demand.

[0084] Taking a dynamic collaboration group consisting of three concrete production bases as an example, the equipment capacity index of base 1 is 85, the material supply sufficiency is 90, the human resource status is 82, and the transportation capacity score is 78; the equipment capacity index of base 2 is 75, the material supply sufficiency is 95, the human resource status is 88, and the transportation capacity score is 85; the equipment capacity index of base 3 is 92, the material supply sufficiency is 80, the human resource status is 75, and the transportation capacity score is 92. According to the global optimization parameters and production capacity indicators, the system allocates tasks for a concrete order of 2,500 cubic meters: base 1 is responsible for 900 cubic meters, base 2 is responsible for 800 cubic meters, and base 3 is responsible for 800 cubic meters.

[0085] The task allocation also considers the concrete variety characteristics and the specialities of each base. For C50 concrete requiring high strength, base 1 with high-strength concrete production experience is preferentially allocated; for impermeable concrete requiring high durability, base 2 with fine control ability is preferentially allocated; for mass concrete, base 3 with temperature control experience is preferentially allocated. Through differentiated allocation, the technical advantages of each base are fully brought into play.

[0086] After the method is applied to actual concrete production management, the production efficiency is improved by 25.7%, the raw material waste is reduced by 18.3%, and the energy consumption is reduced by 15.9%, thereby effectively relieving the production capacity bottleneck problem under the traditional production mode and realizing efficient remote intelligent control and management of the concrete production equipment.

[0087] In a second aspect, the embodiment of the present application provides a remote intelligent control system of a concrete production equipment, comprising: A first unit is configured to collect production parameter data of the concrete production equipment, establish an associated topology graph among the concrete production equipment according to the production parameter data, and determine an associated weight coefficient; calculate a coordination efficiency index of a device group based on the associated weight coefficient, and divide the concrete production equipment into a plurality of dynamic coordination groups according to the coordination efficiency index; A second unit is configured to obtain a concrete production task instruction, calculate a production capacity index of each dynamic coordination group based on the device distribution of the dynamic coordination group; A third unit is configured to allocate a production task for each dynamic coordination group according to the production capacity index, calculate a multi-objective evaluation score based on the completion, construct a target function of an ant colony algorithm based on the multi-objective evaluation score, take the weight coefficient of a control parameter as a decision variable to perform iterative optimization, select an optimal weight coefficient combination obtained after the iterative optimization, and generate an optimal control instruction sequence.

[0088] In a third aspect, the embodiment of the present application provides an electronic device, comprising: a processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described above.

[0089] In a fourth aspect, the embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions are executed by a processor to implement the method described above.

[0090] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions loaded thereon for executing various aspects of the present application.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A remote intelligent control method for concrete production equipment, characterized in that: include: Collect production parameter data of concrete production equipment; Establishing a correlation topology diagram among concrete production equipment according to the production parameter data, and determining correlation weight coefficients; Calculating a coordination efficiency index of the equipment group based on the association weight coefficient, and dividing the concrete production equipment into a plurality of dynamic coordination groups according to the coordination efficiency index; Obtaining concrete production task instructions, and calculating the production capacity index of each dynamic coordination group based on the equipment distribution of the dynamic coordination group; Production tasks are assigned to each dynamic collaborative group according to the production capacity indicators, and a multi-objective evaluation score is calculated based on the completion status. The objective function of the ant colony algorithm is constructed based on the multi-objective evaluation score. The weight coefficients of the control parameters are used as decision variables for iterative optimization. The optimal weight coefficient combination obtained after iterative optimization is selected to generate the optimal control instruction sequence.

2. The method according to claim 1, characterized in that Establishing a correlation topology diagram among concrete production equipment according to the production parameter data and determining correlation weight coefficients includes: Calculating mutual information entropy values ​​at different time delay orders based on the production parameter data, forming a multi-scale mutual information entropy sequence from the mutual information entropy values, and extracting a local maximum point from the multi-scale mutual information entropy sequence as the optimal time delay order; Counting the state transition frequencies of the concrete production equipment in different operating states, and determining the state transition synchronization degree of the concrete production equipment according to the difference between the elements in the state transition probability matrix corresponding to the state transition frequencies; Using the concrete production equipment as nodes and the optimal time delay order and the state transition synchronization degree as edge weights to generate an associated topology graph; Calculate the prediction error of the optimal time delay order and the state migration synchronization degree, determine the weight value based on the prediction error combined with the exponential function, and determine the associated weight coefficient based on the optimal time delay order and the state migration synchronization degree, as well as the weight value.

3. The method according to claim 1, characterized in that The collaborative efficiency index of the equipment group is calculated based on the association weight coefficient, and the concrete production equipment is divided into multiple dynamic collaborative groups according to the collaborative efficiency index, including: The correlation weight coefficients of each concrete production equipment and other concrete production equipment are added and normalized to obtain the synergistic contribution of each concrete production equipment; the synergistic efficiency index is determined based on the correlation weight coefficients of any two concrete production equipment in the group and the synergistic contribution of all concrete production equipment; Concrete production equipment whose collaborative efficiency index is greater than a preset screening threshold is divided into a group to obtain a candidate grouping scheme. The ratio of the number of intersection elements to the number of union elements of the collaborative group member sets at two adjacent moments in the candidate grouping scheme is calculated and added to the group stability index at the previous moment to obtain the group stability index at the current moment. When the group stability index at multiple consecutive moments meets the preset stability threshold, the concrete production equipment is divided into multiple dynamic collaborative groups according to the candidate grouping scheme.

4. The method according to claim 1, wherein Acquire concrete production task instructions, and calculate the production capacity index of each dynamic coordination group based on the equipment distribution of the dynamic coordination group, including: Obtaining the real-time spatial coordinates of each concrete production equipment in the dynamic collaborative group, calculating the spatial distribution center of the dynamic collaborative group based on the real-time spatial coordinates, and using the average distance from each concrete production equipment to the spatial distribution center as the spatial distribution index; Calculating the matching degree between the production parameter data and the production task instruction to obtain the matching degree between devices; The synergy gain coefficient is determined according to the matching degree between the equipment in the dynamic synergy group and the spatial distribution index, and the basic production capacity of the concrete production equipment is multiplied by the weighted function value of the synergy gain coefficient to obtain the production capacity index of each dynamic synergy group.

5. The method according to claim 1, wherein The objective function of the ant colony algorithm is constructed based on the multi-objective evaluation score, and the weight coefficients of the control parameters are used as decision variables for iterative optimization. The optimal weight coefficient combination obtained after iterative optimization is selected to generate the optimal control instruction sequence, including: Initializing the weight coefficients of the ant colony individuals and constructing the objective function of the ant colony algorithm based on the multi-objective evaluation score, recording the local optimal solution and the global optimal solution obtained by the ant colony individuals in the current iteration based on the objective function, multiplying the local optimal solution and the global optimal solution by a preset reinforcement coefficient and adding them together to obtain the current path pheromone concentration; Constructing a set of candidate paths based on the current path pheromone concentration, calculating a dynamic heuristic factor that is adaptively adjusted during the iteration process based on the current iteration progress, and obtaining a selection probability that quantifies the attractiveness of the path by multiplying the current path pheromone concentration by the dynamic heuristic factor and dividing the result by the product of the pheromone concentrations of all paths in the candidate path set; Path sampling is performed based on the selection probability, and an evaluation score of the current path is calculated. A parameter step size is calculated according to the current number of iterations. A weight coefficient of the current path is calculated according to the parameter step size, the evaluation score of the current path, and the historical optimal evaluation score. An optimal control instruction sequence is generated based on the weight coefficient and the preset basic control amount.

6. The method according to claim 5, characterized in that Based on the current iteration progress, a dynamic heuristic factor is calculated and adaptively adjusted during the iteration process, including: Substituting the current iteration progress into the sine wave function to generate a fluctuation period factor; Determine the convergence state based on the optimal objective function value obtained in the current iteration and the historical optimal objective function value, combine the convergence state and the fluctuation period factor and substitute them into the cosine decreasing function to obtain a stage-by-stage attenuation parameter, determine the search intensity parameter based on the objective function values ​​of all paths in the current iteration population, and generate a convergence evaluation value based on the attenuation parameter and the search intensity parameter; Performing Manhattan distance calculation on the paths in the candidate path set, obtaining a relative distance parameter based on a distance distribution characteristic, and performing nonlinear mapping between the relative distance parameter and the convergence evaluation value using a sigmoid function to obtain a diversity adjustment parameter; A dynamic heuristic factor is generated by combining the convergence evaluation value and the diversity adjustment parameter.

7. The method according to claim 1, characterized in that The method further comprises: Collect local operating data and train it to obtain local optimization parameters, which are then encrypted using a differential privacy encryption algorithm. Upload the encrypted local optimization parameters to the cloud for aggregation operations to generate global optimization parameters. Assign production tasks to each dynamic collaborative group based on the global optimization parameters and the production capacity indicators.

8. A remote intelligent control system for concrete production equipment, for implementing the method according to any one of claims 1 to 7, characterized in that: include: The first unit is configured to collect production parameter data of concrete production equipment, establish a correlation topology diagram between the concrete production equipment based on the production parameter data, and determine a correlation weight coefficient; Calculating a coordination efficiency index of the equipment group based on the association weight coefficient, and dividing the concrete production equipment into a plurality of dynamic coordination groups according to the coordination efficiency index; The second unit is used to obtain a concrete production task instruction and calculate a production capacity index of each dynamic coordination group based on the equipment distribution of the dynamic coordination group; The third unit is used to assign production tasks to each dynamic collaborative group according to the production capacity indicators, calculate the multi-objective evaluation score based on the completion status, construct the objective function of the ant colony algorithm based on the multi-objective evaluation score, use the weight coefficients of the control parameters as decision variables for iterative optimization, select the optimal weight coefficient combination obtained after iterative optimization, and generate the optimal control instruction sequence.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

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

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