CN113392512A - Sulfide ore self-heating rate evaluation method and system based on cloud computing - Google Patents
Sulfide ore self-heating rate evaluation method and system based on cloud computing Download PDFInfo
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Abstract
The invention discloses a method and a system for evaluating the self-heating rate of sulfide ore based on cloud computing, wherein the method comprises the following steps: establishing communication connection between a user terminal and a cloud server, sending a request for acquiring environmental parameters and sulfide ore experiment parameters to the user terminal, receiving target environmental parameters and target sulfide ore experiment parameters fed back by the user terminal, selecting a proper target mathematical model in a database according to the target environmental parameters and the target experimental parameters of the sulfide ores, calculating the target self-heating rate of the sulfide ores by combining the target mathematical model with the target environmental parameters and the target experimental parameters of the sulfide ores, greatly saving the labor cost, calculating the target self-heating rate of the sulfide ores by only detecting the self-parameters of the sulfide ores and the external environmental parameters of experimental instruments and combining the selected target mathematical model, improving the working efficiency, the method can avoid the occurrence of the error condition of the calculation result caused by the error of the detection parameters, and ensure the accuracy of the final calculation result.
Description
Technical Field
The invention relates to the technical field of mining safety, in particular to a method and a system for evaluating the self-heating rate of sulfide ore based on cloud computing.
Background
The spontaneous combustion tendency of the sulfide ore is accurately measured, and a basis can be provided for ore deposit mining design so as to correctly select a mining method, a ventilation system, a stoping sequence and take fireproof measures, thereby achieving the purposes of avoiding blind design, saving investment and ensuring safety.
The pyrophoric propensity of an ore refers to the combined pyrophoric propensity of all minerals in the ore, rather than the pyrophoric propensity of a single mineral. The main characteristics of the ore related to the tendency to spontaneous combustion are the material composition of the ore, the structural characteristics of each component, the oxidation speed, the self-heating property, the ignition temperature and the like, wherein the index of the self-heating rate of dynamic oxidation of the ore is particularly important. Although the standard reaction heat of pure sulfide ore can be calculated according to a chemical thermodynamic method, and the combustion heat of impure ore can also be measured by a thermal measurement instrument, the measurements cannot reflect the dynamic oxidation exothermic process of sulfide ore under different temperature conditions and various compound chemical reaction modes, and cannot be used for measuring the oxidation self-heating rate (heat flux) of the ore surface, because the oxidation of the ore on site generally starts from the surface, if the self-heating rate of the dynamic oxidation process of sulfide ore under different temperature conditions can be measured, the method has more practical significance for researching the self-heating tendency of sulfide ore and a fire prevention and extinguishing method, the existing method for calculating the self-heating rate of sulfide ore is that researchers put an ore sample into a specific experimental environment, and the final self-heating rate of the ore sample is calculated by detecting experimental environment parameters and the parameters of the ore sample, however, this method has the following disadvantages: the experimental environment needs to be prepared in advance in each calculation, the preparation process is complicated, and meanwhile, certain errors exist in the detected parameters, so that the labor cost is wasted, and the condition that the final calculation result is inaccurate occurs.
Disclosure of Invention
Aiming at the problems shown above, the invention provides a method and a system for evaluating the self-heating rate of sulfide ore based on cloud computing, which are used for solving the problems that in the background technology, an experimental environment needs to be prepared in advance for each computation, the preparation process is complicated, and meanwhile, the labor cost is wasted and the final computation result is inaccurate due to certain errors of detected parameters.
A method for evaluating the self-heating rate of sulfide ore based on cloud computing comprises the following steps:
establishing communication connection between a user terminal and a cloud server;
sending a request for acquiring environmental parameters and sulfide ore experiment parameters to the user terminal, and receiving target environmental parameters and target sulfide ore experiment parameters fed back by the user terminal;
selecting a proper target mathematical model in a database according to the target environmental parameters and the target sulfide ore experiment parameters;
and calculating the target self-heating rate of the sulfide ore by combining the target mathematical model with the target environmental parameters and the target sulfide ore experiment parameters.
Preferably, the establishing of the communication connection between the user terminal and the cloud server includes:
acquiring a network address of the cloud server by using a preset domain name resolution method;
generating a communication connection request according to the network address, and sending the communication connection request to the cloud server by the user terminal;
acquiring the geographical position of a user terminal, and determining the ip address of the user terminal according to the geographical position;
distributing a dynamic road address for the user terminal according to the ip address;
and the cloud server sends a connection call to the user terminal according to the communication connection request and the dynamic road address, and confirms that the user terminal and the user terminal realize communication connection after responding.
Preferably, the sending a request for obtaining environmental parameters and experimental parameters of the sulfide ore to the user terminal and receiving target environmental parameters and experimental parameters of the target sulfide ore fed back by the user terminal includes:
confirming a plurality of target parameters required for calculating the self-heating rate of the sulfide ore;
classifying the target parameters into a first category and a second category;
after the division is finished, generating an environmental parameter acquisition request according to a first number of first target parameters in a first category, and generating a sulfide ore experiment parameter acquisition request according to a second number of second target parameters in a second category;
and sending the environment parameter acquisition request and the sulfide ore experiment parameter acquisition request to a user terminal, and receiving the target environment parameter and the target sulfide ore experiment parameter fed back by the user terminal.
Preferably, before selecting a suitable target mathematical model in the database based on the target environmental parameters and the target experimental sulfide ore parameters, the method further comprises:
constructing a plurality of preset mathematical models by utilizing a plurality of preset environmental parameters and preset sulfide ore experiment parameters;
setting a parameter selection range of an experimental instrument for each preset mathematical model;
and storing the preset mathematical models after the setting is finished into the database.
Preferably, selecting a suitable target mathematical model in the database according to the target environmental parameters and the target experimental parameters of the sulfide ore comprises:
generating a matching factor according to the target environmental parameter and the target sulfide ore experiment parameter;
matching in the database by using the matching factor to obtain a current preset mathematical model with the first three matching degrees;
obtaining a plurality of parameter vectors in target environmental parameters and target sulfide ore experiment parameters, and screening an expected current preset mathematical model from three current preset mathematical models according to the parameter vectors;
and confirming the expected current preset mathematical model as the target mathematical model.
Preferably, the calculating the target self-heating rate of the sulfide ore by using the target mathematical model and combining the target environmental parameter and the target experimental parameter of the sulfide ore includes:
determining target experimental instrument parameters according to the target mathematical model;
and substituting the target experimental instrument parameters, the target environmental parameters and the target sulfide ore experimental parameters into a preset self-heating rate calculation formula to calculate the target self-heating rate of the sulfide ore.
Preferably, the method further comprises:
verifying the target self-heating rate to obtain a verification result;
judging whether the target self-heating rate is reasonable or not according to the verification result, if so, not needing to carry out subsequent operation, and otherwise, generating an analysis report;
and uploading the analysis report to the user terminal.
Preferably, after a plurality of preset mathematical models are constructed by using a plurality of preset environmental parameters and preset experimental parameters of the sulfide ore, before setting an experimental instrument parameter selection range for each preset mathematical model, the method further comprises:
acquiring a parameter feature vector of each preset mathematical model;
carrying out variable quantity mining on the parameter feature vector of each preset mathematical model to obtain mining variable quantity;
calculating the rigor degree of each preset mathematical model according to the excavation variation of each preset mathematical magic:
wherein p isiExpressed as the stringency of the ith predetermined mathematical model, Qi1Excavation represented as the ith predetermined mathematical modelAmount of variation, Qi2A parameter feature vector, Q, expressed as the ith predetermined mathematical modeli3Characteristic vector of interference information in characteristic vector of parameter expressed as ith preset mathematical model, SiA generalization factor expressed as a parameter feature vector of the ith preset mathematical model, ln expressed as a logarithm, Ti1Expressed as the average reaction time of the ith preset mathematical model in calculating data, Ti2Expressed as the average calculation time length T of the ith preset mathematical model in calculating datai3Expressed as the average required time length theta of the ith preset mathematical model in calculating dataiThe data caching efficiency is expressed as the data caching efficiency of the ith preset mathematical model, e is expressed as a natural constant and takes a value of 2.72, iN is expressed as the number of high-density feature vectors iN the parameter feature vectors of the ith preset mathematical model, and FjA probability distribution expressed as a jth high-density feature vector;
marking a target number of first target preset mathematical models with the rigor degree less than or equal to a preset threshold value, and reconstructing a target number of second target preset mathematical models to replace the plurality of first target preset mathematical models;
after the replacement is finished, acquiring the same parameter characteristic vector between adjacent preset mathematical models;
calculating posterior probability distribution of the same parameter feature vector between two adjacent preset mathematical models;
calculating the target similarity of two adjacent preset mathematical models according to the posterior probability distribution of the same parameter feature vector between the two adjacent preset mathematical models:
wherein D (A, B) represents the target similarity between the ith preset mathematical model and the adjacent B preset mathematical model, M represents the number of the same parameter feature vectors between the A preset mathematical model and the B preset mathematical model,
expressed as a posterior probability distribution of the kth identical parametric feature vector on the a-th predetermined mathematical model component,
expressed as the posterior probability distribution of the kth same parameter feature vector on the component of the B preset mathematical model;
and deleting the latter of the adjacent preset mathematical models with the target similarity being more than or equal to the preset similarity to obtain the final number of preset mathematical models.
A cloud computing-based sulfide ore self-heating rate assessment system, the system comprising:
the establishing module is used for establishing communication connection between the user terminal and the cloud server;
the receiving module is used for sending a request for acquiring environmental parameters and sulfide ore experiment parameters to the user terminal and receiving target environmental parameters and target sulfide ore experiment parameters fed back by the user terminal;
the selection module is used for selecting a proper target mathematical model in a database according to the target environmental parameters and the target sulfide ore experiment parameters;
and the calculation module is used for calculating the target self-heating rate of the sulfide ore by combining the target environment parameter and the target sulfide ore experiment parameter by using the target mathematical model.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a work flow chart of a method for evaluating the self-heating rate of a sulfide ore based on cloud computing according to the present invention;
FIG. 2 is another flowchart of the method for evaluating the self-heating rate of the sulfide ore based on cloud computing according to the present invention;
FIG. 3 is a flowchart illustrating a method for evaluating the self-heating rate of a sulfide ore based on cloud computing according to another embodiment of the present invention;
fig. 4 is a schematic structural diagram of a system for evaluating the self-heating rate of a sulfide ore based on cloud computing according to the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The spontaneous combustion tendency of the sulfide ore is accurately measured, and a basis can be provided for ore deposit mining design so as to correctly select a mining method, a ventilation system, a stoping sequence and take fireproof measures, thereby achieving the purposes of avoiding blind design, saving investment and ensuring safety.
The pyrophoric propensity of an ore refers to the combined pyrophoric propensity of all minerals in the ore, rather than the pyrophoric propensity of a single mineral. The main characteristics of the ore related to the tendency to spontaneous combustion are the material composition of the ore, the structural characteristics of each component, the oxidation speed, the self-heating property, the ignition temperature and the like, wherein the index of the self-heating rate of dynamic oxidation of the ore is particularly important. Although the standard reaction heat of pure sulfide ore can be calculated according to a chemical thermodynamic method, and the combustion heat of impure ore can also be measured by a thermal measurement instrument, the measurements cannot reflect the dynamic oxidation exothermic process of sulfide ore under different temperature conditions and various compound chemical reaction modes, and cannot be used for measuring the oxidation self-heating rate (heat flux) of the ore surface, because the oxidation of the ore on site generally starts from the surface, if the self-heating rate of the dynamic oxidation process of sulfide ore under different temperature conditions can be measured, the method has more practical significance for researching the self-heating tendency of sulfide ore and a fire prevention and extinguishing method, the existing method for calculating the self-heating rate of sulfide ore is that researchers put an ore sample into a specific experimental environment, and the final self-heating rate of the ore sample is calculated by detecting experimental environment parameters and the parameters of the ore sample, however, this method has the following disadvantages: the experimental environment needs to be prepared in advance in each calculation, the preparation process is complex, meanwhile, due to the fact that certain errors exist in detected parameters, labor cost is wasted, and the situation that the final calculation result is inaccurate occurs.
A method for evaluating the self-heating rate of sulfide ore based on cloud computing is shown in figure 1 and comprises the following steps:
s101, establishing communication connection between a user terminal and a cloud server;
step S102, sending a request for obtaining environmental parameters and sulfide ore experiment parameters to the user terminal, and receiving target environmental parameters and target sulfide ore experiment parameters fed back by the user terminal;
s103, selecting a proper target mathematical model in a database according to the target environmental parameters and the target sulfide ore experiment parameters;
step S104, calculating a target self-heating rate of the sulfide ore by combining the target environment parameter and the target sulfide ore experiment parameter by using the target mathematical model;
in this embodiment, the environmental parameters include environmental parameters outside the experimental instrument, the target sulfide ore experimental parameters include sulfide ore self parameters, the target mathematical model already includes environmental parameters inside the experimental instrument and sulfide ore experimental parameters, and the target self-heating rate of the sulfide ore can be calculated by detecting the sulfide ore self parameters and the environmental parameters outside the experimental instrument in combination with the model.
The working principle of the technical scheme is as follows: the method comprises the steps of establishing communication connection between a user terminal and a cloud server, sending a request for obtaining environmental parameters and sulfide ore experiment parameters to the user terminal, receiving target environmental parameters and target sulfide ore experiment parameters fed back by the user terminal, selecting a proper target mathematical model in a database according to the target environmental parameters and the target sulfide ore experiment parameters, and calculating the target self-heating rate of sulfide ore by combining the target environmental parameters and the target sulfide ore experiment parameters through the target mathematical model.
The beneficial effects of the above technical scheme are: the method has the advantages that the experimental model is stored by the cloud server, so that a researcher does not need to prepare experimental environments in advance each time, manpower cost is greatly saved, meanwhile, the researcher can calculate the target self-heating rate of the sulfide ore only by detecting the parameters of the sulfide ore and the environmental parameters outside the experimental instrument and combining the selected target mathematical model, the working efficiency is improved, furthermore, the experimental parameters in each mathematical model are obtained by experiment and verification for countless times, the precision of the experimental parameters is higher than the precision of the detected parameters, the error condition of the calculated result caused by the error of the detected parameters can be avoided, the accuracy of the final calculated result is ensured, the problem that the experimental environments need to be prepared in advance each time in the prior art is solved, the preparation process is complicated, and meanwhile, the manpower cost is wasted and the final calculated result is inaccurate due to the certain error of the detected parameters is solved The occurrence of problems.
In one embodiment, the establishing of the communication connection between the user terminal and the cloud server includes:
acquiring a network address of the cloud server by using a preset domain name resolution method;
generating a communication connection request according to the network address, and sending the communication connection request to the cloud server by the user terminal;
acquiring the geographical position of a user terminal, and determining the ip address of the user terminal according to the geographical position;
distributing a dynamic road address for the user terminal according to the ip address;
and the cloud server sends a connection call to the user terminal according to the communication connection request and the dynamic road address, and confirms that the user terminal and the user terminal realize communication connection after responding.
The beneficial effects of the above technical scheme are: the accuracy of mutual connection between the user terminal and the cloud server is guaranteed by mutually confirming respective addresses of the user terminal and the cloud server, furthermore, the initial state of connection at each time can be kept by distributing dynamic road addresses to the user terminal, the situation that the user terminal is connected with the cloud server privately and steals data randomly is avoided, and the safety and confidentiality of data stored in the cloud server are guaranteed.
In an embodiment, as shown in fig. 2, the sending a request for obtaining environmental parameters and experimental parameters of a sulfide ore to the user terminal and receiving target environmental parameters and experimental parameters of a sulfide ore fed back by the user terminal includes:
step S201, confirming a plurality of target parameters required for calculating the self-heating rate of the sulfide ore;
step S202, classifying the target parameters into a first category and a second category;
step S203, after the division is finished, generating an environmental parameter acquisition request according to a first number of first target parameters in a first category, and generating a sulfide ore experiment parameter acquisition request according to a second number of second target parameters in a second category;
step S204, sending the environment parameter acquisition request and the sulfide ore experiment parameter acquisition request to a user terminal, and receiving the target environment parameter and the target sulfide ore experiment parameter fed back by the user terminal.
The beneficial effects of the above technical scheme are: the target parameters are classified to generate the acquisition request, so that the same type of parameters can be summarized together to send the request, the condition that part of the parameters are missed is avoided, and the integrity of data is ensured.
In one embodiment, prior to selecting a suitable target mathematical model in the database based on the target environmental parameters and target experimental sulfide ore parameters, the method further comprises:
constructing a plurality of preset mathematical models by utilizing a plurality of preset environmental parameters and preset sulfide ore experiment parameters;
setting a parameter selection range of an experimental instrument for each preset mathematical model;
and storing the preset mathematical models after the setting is finished into the database.
The beneficial effects of the above technical scheme are: through setting the parameter selection range of the experimental instrument for each preset mathematical model, researchers can select different experimental instrument parameters on the same model according to different research contents without constructing a plurality of models of the same experimental instrument, the data storage capacity of a cloud server is reduced, the memory of a database is saved, and meanwhile, the efficiency of calling the model is improved.
In one embodiment, as shown in fig. 3, selecting a suitable target mathematical model in the database based on the target environmental parameters and the target experimental sulfide ore parameters includes:
s301, generating a matching factor according to the target environment parameter and the target sulfide ore experiment parameter;
step S302, matching is carried out in the database by utilizing the matching factors to obtain a current preset mathematical model with the first three matching degrees;
s303, obtaining a plurality of parameter vectors in target environmental parameters and target sulfide ore experiment parameters, and screening an expected current preset mathematical model from three current preset mathematical models according to the parameter vectors;
and step S304, confirming the expected current preset mathematical model as the target mathematical model.
The beneficial effects of the above technical scheme are: the matching factors are generated to match the preset mathematical model, so that the current preset mathematical model matched with the target environment parameters and the target sulfide ore experiment parameters can be obtained without careless mistakes, the matching efficiency is improved, further, the expected current preset mathematical model is screened out according to the parameter vectors, the target mathematical model corresponding to the target environment parameters and the target sulfide ore experiment parameters can be determined more carefully by utilizing the characteristic vectors, the missing recognition condition is avoided, and the working efficiency is further improved.
In one embodiment, said calculating a target self-heating rate of the sulfide ore using said target mathematical model in combination with said target environmental parameters and target experimental sulfide ore parameters comprises:
determining target experimental instrument parameters according to the target mathematical model;
substituting the target experimental instrument parameters, the target environmental parameters and the target sulfide ore experimental parameters into a preset self-heating rate calculation formula to calculate the target self-heating rate of the sulfide ore;
in this embodiment, the calculating the target self-heating rate of the sulfide ore by using the preset self-heating rate calculation formula includes:
the oxidation self-heating rate of the sulfide ore in unit time is calculated according to the following formula:
wherein q issExpressed as the oxidative self-heating rate of the sulphide ore per unit time, d is expressed as the mean diameter of the sulphide ore sample particles in the experimental parameters of the target sulphide ore, t1Expressed as the temperature, t, of the centre of the sulphide ore sample in the experimental parameters of the target sulphide ore2Expressed as the temperature, r, of the air outside the target laboratory instrument in the target environmental parameter1Expressed as the radius, r, of the thermocouple porcelain insulator in the parameters of the target experimental instrument2Expressed as the inner radius, r, of the glass reactor of the target laboratory apparatus in the parameters of the target laboratory apparatus3Expressed as the outer radius, μ, of the glass reactor of the target laboratory instrument in the parameters of the target laboratory instrumentgExpressed as the glass thermal conductivity of the target experimental instrument in the parameters of the target experimental instrument, ln is expressed as logarithm, and beta is expressed as glassNatural convection heat transfer coefficient, mu, between the outer wall of the glass reactor and the air0Expressed as the thermal conductivity of the sulphide ore sample in the experimental parameters of the target sulphide ore;
the beneficial effects of the above technical scheme are: target experimental instrument parameters required by researchers can be rapidly determined by utilizing the target mathematical model, and then the target self-heating rate of the sulfide ore can be accurately calculated through a preset formula, so that the labor cost is further saved.
In one embodiment, the method further comprises:
verifying the target self-heating rate to obtain a verification result;
judging whether the target self-heating rate is reasonable or not according to the verification result, if so, not needing to carry out subsequent operation, and otherwise, generating an analysis report;
and uploading the analysis report to the user terminal.
The beneficial effects of the above technical scheme are: the cloud computing result can be verified artificially by verifying the target self-heating rate to judge whether the target self-heating rate is reasonable or not, the objectivity of the final computing result is ensured, and furthermore, a research staff can improve and modify own experimental scheme adaptively by generating an analysis report and sending the analysis report to a user terminal, so that a data basis is provided for the subsequent calculation of the self-heating rate of the sulfide ore.
In one embodiment, after constructing a plurality of preset mathematical models using a plurality of preset environmental parameters and preset sulfide ore experiment parameters, before setting the experimental instrument parameter selection range for each preset mathematical model, the method further comprises:
acquiring a parameter feature vector of each preset mathematical model;
carrying out variable quantity mining on the parameter feature vector of each preset mathematical model to obtain mining variable quantity;
calculating the rigor degree of each preset mathematical model according to the excavation variation of each preset mathematical magic:
wherein p isiExpressed as the stringency of the ith predetermined mathematical model, Qi1Expressed as the excavation variation, Q, of the ith predetermined mathematical modeli2A parameter feature vector, Q, expressed as the ith predetermined mathematical modeli3Characteristic vector of interference information in characteristic vector of parameter expressed as ith preset mathematical model, SiA generalization factor expressed as a parameter feature vector of the ith preset mathematical model, ln expressed as a logarithm, Ti1Expressed as the average reaction time of the ith preset mathematical model in calculating data, Ti2Expressed as the average calculation time length T of the ith preset mathematical model in calculating datai3Expressed as the average required time length theta of the ith preset mathematical model in calculating dataiThe data caching efficiency is expressed as the data caching efficiency of the ith preset mathematical model, e is expressed as a natural constant and takes a value of 2.72, iN is expressed as the number of high-density feature vectors iN the parameter feature vectors of the ith preset mathematical model, and FjA probability distribution expressed as a jth high-density feature vector;
marking a target number of first target preset mathematical models with the rigor degree less than or equal to a preset threshold value, and reconstructing a target number of second target preset mathematical models to replace the plurality of first target preset mathematical models;
after the replacement is finished, acquiring the same parameter characteristic vector between adjacent preset mathematical models;
calculating posterior probability distribution of the same parameter feature vector between two adjacent preset mathematical models;
calculating the target similarity of two adjacent preset mathematical models according to the posterior probability distribution of the same parameter feature vector between the two adjacent preset mathematical models:
wherein D (A, B) is represented by the sum of the ith preset mathematical modelThe target similarity between adjacent B-th preset mathematical models is expressed as the quantity of the same parameter characteristic vectors between the A-th preset mathematical model and the B-th preset mathematical model,
expressed as a posterior probability distribution of the kth identical parametric feature vector on the a-th predetermined mathematical model component,
expressed as the posterior probability distribution of the kth same parameter feature vector on the component of the B preset mathematical model;
and deleting the latter of the adjacent preset mathematical models with the target similarity being more than or equal to the preset similarity to obtain the final number of preset mathematical models.
The beneficial effects of the above technical scheme are: the qualification of each preset mathematical model for data processing can be effectively evaluated by calculating the rigor degree of each preset mathematical model, so that each preset mathematical model can be guaranteed to realize accurate calculation of the self-heating rate of the sulfide ore, the stability is improved, furthermore, the occurrence of the same model can be avoided by calculating the similarity between two adjacent preset mathematical models, and the running stability is further guaranteed.
The embodiment also discloses a system for evaluating the self-heating rate of sulfide ore based on cloud computing, as shown in fig. 4, the system includes:
an establishing module 401, configured to establish a communication connection between a user terminal and a cloud server;
a receiving module 402, configured to send a request for obtaining an environmental parameter and a sulfide ore experiment parameter to the user terminal, and receive a target environmental parameter and a target sulfide ore experiment parameter fed back by the user terminal;
a selecting module 403, configured to select a suitable target mathematical model from a database according to the target environmental parameter and the target sulfide ore experiment parameter;
a calculating module 404, configured to calculate a target self-heating rate of the sulfide ore by using the target mathematical model in combination with the target environmental parameter and the target experimental parameter of the sulfide ore.
The working principle and the advantageous effects of the above technical solution have been explained in the method claims, and are not described herein again.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (9)
1. A method for evaluating the self-heating rate of sulfide ore based on cloud computing is characterized by comprising the following steps:
establishing communication connection between a user terminal and a cloud server;
sending a request for acquiring environmental parameters and sulfide ore experiment parameters to the user terminal, and receiving target environmental parameters and target sulfide ore experiment parameters fed back by the user terminal;
selecting a proper target mathematical model in a database according to the target environmental parameters and the target sulfide ore experiment parameters;
and calculating the target self-heating rate of the sulfide ore by combining the target mathematical model with the target environmental parameters and the target sulfide ore experiment parameters.
2. The method for assessing self-heating rate of sulfide ore based on cloud computing according to claim 1, wherein the establishing of the communication connection between the user terminal and the cloud server comprises:
acquiring a network address of the cloud server by using a preset domain name resolution method;
generating a communication connection request according to the network address, and sending the communication connection request to the cloud server by the user terminal;
acquiring the geographical position of a user terminal, and determining the ip address of the user terminal according to the geographical position;
distributing a dynamic road address for the user terminal according to the ip address;
and the cloud server sends a connection call to the user terminal according to the communication connection request and the dynamic road address, and confirms that the user terminal and the user terminal realize communication connection after responding.
3. The method for evaluating the self-heating rate of the sulfide ore based on the cloud computing as claimed in claim 1, wherein the sending a request for obtaining the environmental parameters and the experimental parameters of the sulfide ore to the user terminal and receiving the target environmental parameters and the experimental parameters of the sulfide ore fed back by the user terminal comprises:
confirming a plurality of target parameters required for calculating the self-heating rate of the sulfide ore;
classifying the target parameters into a first category and a second category;
after the division is finished, generating an environmental parameter acquisition request according to a first number of first target parameters in a first category, and generating a sulfide ore experiment parameter acquisition request according to a second number of second target parameters in a second category;
and sending the environment parameter acquisition request and the sulfide ore experiment parameter acquisition request to a user terminal, and receiving the target environment parameter and the target sulfide ore experiment parameter fed back by the user terminal.
4. The cloud-computing-based method of assessing self-heating rate of a sulfide ore according to claim 1, wherein prior to selecting a suitable target mathematical model in a database based on the target environmental parameters and target experimental sulfide ore parameters, the method further comprises:
constructing a plurality of preset mathematical models by utilizing a plurality of preset environmental parameters and preset sulfide ore experiment parameters;
setting a parameter selection range of an experimental instrument for each preset mathematical model;
and storing the preset mathematical models after the setting is finished into the database.
5. The cloud-computing-based method for assessing the self-heating rate of a sulfide ore according to claim 4, wherein selecting an appropriate target mathematical model in a database according to the target environmental parameters and target experimental sulfide ore parameters comprises:
generating a matching factor according to the target environmental parameter and the target sulfide ore experiment parameter;
matching in the database by using the matching factor to obtain a current preset mathematical model with the first three matching degrees;
obtaining a plurality of parameter vectors in target environmental parameters and target sulfide ore experiment parameters, and screening an expected current preset mathematical model from three current preset mathematical models according to the parameter vectors;
and confirming the expected current preset mathematical model as the target mathematical model.
6. The method for assessing self-heating rate of sulfide ore based on cloud computing according to claim 1, wherein the calculating the target self-heating rate of sulfide ore by using the target mathematical model in combination with the target environmental parameters and the target experimental parameters of sulfide ore comprises:
determining target experimental instrument parameters according to the target mathematical model;
and substituting the target experimental instrument parameters, the target environmental parameters and the target sulfide ore experimental parameters into a preset self-heating rate calculation formula to calculate the target self-heating rate of the sulfide ore.
7. The cloud-computing-based method for assessing the self-heating rate of a sulfide ore according to claim 1, wherein the method further comprises:
verifying the target self-heating rate to obtain a verification result;
judging whether the target self-heating rate is reasonable or not according to the verification result, if so, not needing to carry out subsequent operation, and otherwise, generating an analysis report;
and uploading the analysis report to the user terminal.
8. The cloud-computing-based method for assessing the self-heating rate of a sulfide ore according to claim 4, wherein after constructing a plurality of preset mathematical models using a plurality of preset environmental parameters and preset experimental parameters of a sulfide ore, before setting a selection range of experimental instrument parameters for each preset mathematical model, the method further comprises:
acquiring a parameter feature vector of each preset mathematical model;
carrying out variable quantity mining on the parameter feature vector of each preset mathematical model to obtain mining variable quantity;
calculating the rigor degree of each preset mathematical model according to the excavation variation of each preset mathematical magic:
wherein p isiExpressed as the stringency of the ith predetermined mathematical model, Qi1Expressed as the excavation variation, Q, of the ith predetermined mathematical modeli2A parameter feature vector, Q, expressed as the ith predetermined mathematical modeli3Characteristic vector of interference information in characteristic vector of parameter expressed as ith preset mathematical model, SiA generalization factor expressed as a parameter feature vector of the ith preset mathematical model, ln expressed as a logarithm, Ti1Expressed as the average reaction time of the ith preset mathematical model in calculating data, Ti2Expressed as the average calculation time length T of the ith preset mathematical model in calculating datai3Expressed as the average of the ith preset mathematical model in calculating the dataLength of time required, thetaiThe data caching efficiency is expressed as the data caching efficiency of the ith preset mathematical model, e is expressed as a natural constant and takes a value of 2.72, iN is expressed as the number of high-density feature vectors iN the parameter feature vectors of the ith preset mathematical model, and FjA probability distribution expressed as a jth high-density feature vector;
marking a target number of first target preset mathematical models with the rigor degree less than or equal to a preset threshold value, and reconstructing a target number of second target preset mathematical models to replace the plurality of first target preset mathematical models;
after the replacement is finished, acquiring the same parameter characteristic vector between adjacent preset mathematical models;
calculating posterior probability distribution of the same parameter feature vector between two adjacent preset mathematical models;
calculating the target similarity of two adjacent preset mathematical models according to the posterior probability distribution of the same parameter feature vector between the two adjacent preset mathematical models:
wherein D (A, B) represents the target similarity between the ith preset mathematical model and the adjacent B preset mathematical model, M represents the number of the same parameter feature vectors between the A preset mathematical model and the B preset mathematical model,
expressed as a posterior probability distribution of the kth identical parametric feature vector on the a-th predetermined mathematical model component,
expressed as the posterior probability distribution of the kth same parameter feature vector on the component of the B preset mathematical model;
and deleting the latter of the adjacent preset mathematical models with the target similarity being more than or equal to the preset similarity to obtain the final number of preset mathematical models.
9. A system for evaluating the self-heating rate of sulfide ore based on cloud computing is characterized by comprising:
the establishing module is used for establishing communication connection between the user terminal and the cloud server;
the receiving module is used for sending a request for acquiring environmental parameters and sulfide ore experiment parameters to the user terminal and receiving target environmental parameters and target sulfide ore experiment parameters fed back by the user terminal;
the selection module is used for selecting a proper target mathematical model in a database according to the target environmental parameters and the target sulfide ore experiment parameters;
and the calculation module is used for calculating the target self-heating rate of the sulfide ore by combining the target environment parameter and the target sulfide ore experiment parameter by using the target mathematical model.
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