Production management system of mining temperature sensor

Abstract

The invention discloses a production management system of a mining temperature sensor, relates to the technical field of production management, and solves the technical problem that the mining temperature sensor in the prior art is easy to generate errors and faults in a complex mine environment, but is difficult to calibrate and overhaul due to the complex and changeable mine environment. The method comprises the following steps: inputting a historical reject ratio data set into a probability prediction model to obtain a reject ratio; determining the number of pre-produced orders according to the reject ratio; grading the reject ratio, and determining the proportion of rechecking the number of pre-produced orders according to the graded grades; acquiring an actual use environment of a client to the temperature sensor, partitioning the actual use environment, and performing environment simulation by using a digital twinborn model; judging whether the temperature sensing effect of the temperature sensor is weakened due to environmental reasons or not; if yes, error correction is carried out; if not, continuing to judge; according to the invention, the mining temperature sensor can be put into use with the minimum error through multiple times of reinspection.

Description

Technical Field

[0001] The invention belongs to the field of mechanical engineering and relates to production management technology, in particular to a production management system for a temperature sensor for mining. Background Art

[0002] The production management system can monitor the production process of mining temperature sensors in real time, including raw material procurement, production and processing, quality inspection and other links; through automation and intelligent means, the system can optimize the production process, reduce manual intervention, and thus improve production efficiency; the production management system can achieve accurate inventory management of raw materials for mining temperature sensors; resource scheduling optimization: the system can automatically schedule production resources according to production plans and equipment status, which is conducive to the rational use of production resources; through strict quality control of mining temperature sensors, it helps managers to discover and solve quality problems in a timely manner and ensure the stability of product quality.

[0003] In the prior art, temperature sensors used in mines are prone to errors and failures in complex environments such as mines. However, due to the complex and changeable environment of mines, it is difficult to calibrate and repair them. In addition, some sensors are damaged without considering the harsh environment of mines and need to be replaced or repaired, which increases the complexity and cost of maintenance.

[0004] The present invention provides a production management system for mining temperature sensors to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a production management system for mine temperature sensors, which is used to solve the technical problems in the prior art that mine temperature sensors are prone to errors and failures in a complex environment such as a mine, but are difficult to calibrate and repair due to the complex and changeable mine environment; and some sensors are damaged without considering the harsh mine environment and need to be replaced or repaired, which increases the complexity and cost of maintenance.

[0006] To achieve the above-mentioned object, the first aspect of the present invention provides a production management system for a temperature sensor for mining, comprising: a data storage module, a production management module, a detection management module, a system management module, and a safety and environmental management module;

[0007] Data storage module: used to store the order information of mining temperature sensors and historical unqualified rate data sets; the order information includes: supplier information, procurement information and raw materials required for the order;

[0008] Production management module: inputting the historical non-conforming rate data set into the probability prediction model to obtain the non-conforming rate; determining the number of pre-production orders based on the non-conforming rate; wherein the probability prediction model is built based on the artificial intelligence model; and,

[0009] The non-conforming rate is divided into grades, and the proportion of pre-production orders that need to be re-inspected is determined according to the grades;

[0010] Detection management module: obtain the customer's actual use environment of the temperature sensor, divide the actual use environment into zones, and use the digital twin model to simulate the environment; determine whether the temperature sensor's temperature sensing effect is weakened due to environmental reasons; if yes, perform error correction; if no, continue to judge;

[0011] System management module: Create three major units to set up the system and complete the production management process.

[0012] Preferably, the system also includes: a safety and environmental management module: used to regulate the production process to comply with safety standards and environmental requirements; monitor safety hazards and environmental pollution problems in the production process, and regularly train and publicize safety and environmental knowledge.

[0013] The present invention can significantly reduce the occurrence of workplace accidents and protect the health and life safety of employees by regulating the production process to meet safety standards; and help protect natural resources and ecosystems and promote sustainable development by monitoring and reducing environmental pollution during the production process.

[0014] Preferably, the step of inputting the historical failure rate data set into the probability prediction model to obtain the failure rate includes:

[0015] Acquire a historical failure rate data set from a database, and mark the historical failure rate data set as an abnormal identification sequence;

[0016] Retrieve the probability prediction model, input the anomaly recognition sequence into the probability prediction model, and obtain the anomaly label; match the corresponding anomaly type according to the anomaly label; match the corresponding unqualified rate according to the anomaly label and mark the unqualified rate as NP; wherein the anomaly label is set to a positive integer.

[0017] The present invention can improve the accuracy of anomaly detection based on a large amount of data training by using a probability prediction model; it can not only detect anomalies, but also match specific anomaly types according to anomaly labels, which helps to deeply analyze the causes of anomalies and take targeted measures.

[0018] Preferably, the probability prediction model is constructed based on an artificial intelligence model, including:

[0019] Acquire standard training data; wherein the standard training data includes standard input data consistent with the content attributes of the abnormal recognition sequence, and standard output data consistent with the content attributes of the abnormal label;

[0020] The artificial intelligence model is trained using standard training data, and the trained artificial intelligence model is marked as an abnormal prediction model; wherein the artificial intelligence model includes a convolutional neural network model or a long short-term memory neural network model.

[0021] Preferably, determining the number of pre-production orders according to the non-conforming rate includes:

[0022] Retrieve the failure rate NP, obtain the number of user orders from the database and mark it as DT, and mark the safety factor as α; where 0<α≤1;

[0023] The number of pre-production orders is calculated using the formula DS = LC × (1 + NP) × α;

[0024] The safety margin is calculated using the formula AY=DT-DS.

[0025] It should be noted that the products used as safety margins are used as replacement products for unqualified products in user orders.

[0026] The present invention compensates for the uncertainty of the prediction by adding a certain safety margin on the basis of the predicted defective rate; moreover, the calculation of the safety margin helps to ensure that there are enough products to replace defective products and reduce the waste caused by product quality problems; by accurately calculating the production quantity and safety margin, the enterprise can better control the production cost and avoid the cost increase caused by overproduction or inventory backlog.

[0027] Preferably, the determining the proportion of the number of pre-production orders that need to be re-inspected according to the divided levels includes:

[0028] Retrieve the non-conforming rate NP, set the non-conforming rate thresholds of three levels as T1, T2, and T3 respectively; mark the number of orders that need to be re-inspected as ɡ and β; where 0≤T1<T2≤0.05<T3<1; 0≤NP≤1; 0<ɡ<β<1;

[0029] When 0≤NP≤T1, there is no need to recheck the number of pre-production orders;

[0030] When T1<NP≤T2, recheck ɡ% of the pre-production orders by simple random sampling method;

[0031] When T2<NP, recheck β% of the pre-produced orders by simple random sampling method;

[0032] When NP=T3, all pre-production orders need to be rechecked.

[0033] It should be noted that the failure rate thresholds for the two levels and the specific numerical settings of ɡ and β are set by the staff based on actual conditions.

[0034] By setting two different failure rate thresholds, the present invention allows enterprises to flexibly decide whether re-inspection is needed and the re-inspection ratio based on the actual failure rate; this not only avoids the cost increase caused by re-inspecting all products, but also reduces unnecessary workload and improves production efficiency.

[0035] Preferably, partitioning the actual use environment includes:

[0036] Obtain the distribution map of temperature sensors in the mine from the database; install several temperature sensors according to the distribution map of temperature sensors; collect real-time temperatures at various locations in the mine through temperature sensors; divide the environment in the mine into six types of areas; the six types of areas include: explosive areas, dusty areas, humid areas, high-temperature areas, areas containing corrosive gases, and areas with high-intensity vibrations;

[0037] When the temperature sensor installation location in the mine includes any Class 1-2 area, the corresponding temperature sensor is marked as Class A;

[0038] When the installation location of the temperature sensor in the mine includes any category 3-4 area, the corresponding temperature sensor is marked as level B;

[0039] When the installation location of the temperature sensor in the mine includes any category 5-6 area, the corresponding temperature sensor is marked as level C;

[0040] Among them, level A<level B<level C.

[0041] The present invention can ensure more targeted temperature monitoring of key areas by installing different temperature sensors according to different mining environment characteristics; and can help better observe the error of the temperature sensor by dividing the areas such as explosive areas and high temperature areas.

[0042] Preferably, the determining whether the temperature sensing effect of the temperature sensor is weakened due to environmental reasons includes:

[0043] Determine in sequence in a set order whether the temperature sensors installed in the corresponding areas in different levels are responded to by the corresponding responders;

[0044] If yes, the temperature sensing effect of the temperature sensor installed in the corresponding area is not weakened;

[0045] If no, determine whether an early warning message can be issued when the temperature exceeds the standard range of the ventilation system; if yes, the temperature sensing effect of the temperature sensor installed in the corresponding area has not been weakened; if no, the temperature sensing effect of the temperature sensor installed in the corresponding area has been weakened.

[0046] It should be noted that the specific order of judgment is set by the staff based on actual work experience.

[0047] The present invention can issue early warning information in time if the temperature exceeds the standard range of the ventilation system, which helps to take measures quickly to avoid accidents; by judging the temperature sensing effect of the sensor, maintenance and replacement resources can be reasonably configured to ensure that the monitoring equipment in key areas is always in the best condition, which is conducive to further error correction later.

[0048] Preferably, the error correction comprises:

[0049] According to the actual use environment of different mining areas, formulate the qualification standards for corresponding regional products, and optimize product design according to the qualification standards; among which, optimizing product design includes: using corrosion-resistant materials, enhancing sealing performance, strengthening quality control, improving production processes, and strengthening employee training.

[0050] The present invention customizes qualification standards for different mining environments, which is conducive to enabling mining temperature sensors to better adapt to specific environments and improve their effectiveness and reliability in practical applications.

[0051] Preferably, the creation of three major units to set up the system to complete the production management process includes:

[0052] Authority allocation unit: including allocating authority to staff members, and tracking and auditing employees’ operation records;

[0053] Product management unit: records the product's warehousing information, outbound information, and inventory information after production;

[0054] Coding management unit: encode the products entering the warehouse.

[0055] The present invention ensures that employees are aware of their work scope and responsibilities by limiting their authority; by coding the products entering the warehouse, when there are problems with product quality, managers can quickly locate the production batches and flow directions of the problematic products through the system, and take recall measures in a timely manner to ensure the traceability of the products.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The present invention centrally stores order information and historical unqualified rate data sets through the data storage module, providing a solid data foundation for subsequent production management, quality inspection and system management; the production management module uses a probability prediction model to predict the unqualified rate based on the historical unqualified rate data set, which helps enterprises plan production in advance and avoid excessive or insufficient production; through the classification of unqualified rates, enterprises can set different re-inspection ratios, which not only ensures product quality but also controls inspection costs.

[0058] 2. In the present invention, the actual usage environment of the temperature sensor by the customer is obtained through the detection management module, and the digital twin model is used for environmental simulation, which can accurately judge the changes in the environment on the temperature sensing effect of the temperature sensor, which is helpful to discover and solve potential quality problems in advance; the authority allocation unit in the system management module allocates authority to the staff to improve the security of the system; the product management unit records the warehousing, outbound and inventory information of the product after production, which is helpful for the enterprise to achieve the full life cycle management of the product; the coding management unit encodes the warehoused products, which is helpful for the enterprise to track and recall products more effectively; the present invention ensures that the mining temperature sensor can be put into use with minimal error by conducting multiple re-inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 This is a schematic diagram of the relationship between modules included in the present invention;

[0061] Figure 2 It is a schematic diagram of the implementation process of the present invention;

[0062] Figure 3 It is a schematic diagram of the specific steps of production management of the present invention;

[0063] Figure 4 It is a schematic diagram of the specific steps of detection management of the present invention. DETAILED DESCRIPTION

[0064] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] See also Figure 1-Figure 2 , the first aspect of the present invention provides a production management system for a temperature sensor for a mine, including: a data storage module, a production management module, a detection management module, a system management module, and a safety and environmental management module;

[0066] Data storage module: used to store the order information of mining temperature sensors and historical unqualified rate data sets; the order information includes: supplier information, procurement information and raw materials required for the order;

[0067] Production management module: inputting the historical non-conforming rate data set into the probability prediction model to obtain the non-conforming rate; determining the number of pre-production orders based on the non-conforming rate; wherein the probability prediction model is built based on the artificial intelligence model; and,

[0068] The non-conforming rate is divided into grades, and the proportion of pre-production orders that need to be re-inspected is determined according to the grades;

[0069] Detection management module: obtain the customer's actual use environment of the temperature sensor, divide the actual use environment into zones, and use the digital twin model to simulate the environment; determine whether the temperature sensor's temperature sensing effect is weakened due to environmental reasons; if yes, perform error correction; if no, continue to judge;

[0070] System management module: Create three major units to set up the system and complete the production management process.

[0071] See also Figure 3 ,The specific steps of production management are to obtain the historical ,dataset of non-conformance rate from the database and mark the historical ,dataset of non-conformance rate as an anomaly recognition sequence;

[0072] Retrieve the probability prediction model, input the anomaly recognition sequence into the probability prediction model, and obtain an anomaly label; match the corresponding anomaly type according to the anomaly label; match the corresponding unqualified rate according to the anomaly label and mark the unqualified rate as NP; wherein the anomaly label is set to a positive integer; obtain standard training data; wherein the standard training data includes standard input data consistent with the content attributes of the anomaly recognition sequence, and standard output data consistent with the content attributes of the anomaly label;

[0073] Using standard training data to train an artificial intelligence model, and marking the trained artificial intelligence model as an abnormal prediction model; wherein the artificial intelligence model includes a convolutional neural network model or a long short-term memory neural network model;

[0074] Retrieve the failure rate NP, obtain the number of user orders from the database and mark it as DT, and mark the safety factor as α; where 0<α≤1;

[0075] The number of pre-production orders is calculated using the formula DS = LC × (1 + NP) × α;

[0076] The safety margin is calculated by the formula AY=DT-DS;

[0077] Retrieve the non-conforming rate NP, set the non-conforming rate thresholds of three levels as T1, T2, and T3 respectively; mark the number of orders that need to be re-inspected as ɡ and β; where 0≤T1<T2≤0.05<T3<1; 0≤NP≤1; 0<ɡ<β<1;

[0078] When 0≤NP≤T1, there is no need to recheck the number of pre-production orders;

[0079] When T1<NP≤T2, recheck ɡ% of the pre-production orders by simple random sampling method;

[0080] When T2<NP, recheck β% of the pre-produced orders by simple random sampling method;

[0081] When NP=T3, all pre-production orders need to be rechecked.

[0082] For example, there are 10,000 orders for a temperature sensor for a mine. Now we retrieve a set of historical unqualified rate data sets [6.04%, 4.53%, 2.33%, 2.13%, 1.95%] that are closest to the order time. We mark the data sets as an abnormal recognition sequence and input them into the probability prediction model. We match the corresponding unqualified rate according to the abnormal label and set the unqualified rate to 1.85%.

[0083] Set the safety factor to 1, and calculate the number of pre-production orders to be 10,185 pieces using the formula DS = 1000 × (1 + 1.85%) × 1

[0084] The three levels of unqualified rate thresholds are set as 1% for T1, 4% for T2, and 8% for T3;

[0085] Since T1 = 1% < 1.85% < T2 = 4%, it is set to re-inspect 10% of the pre-produced orders through simple random sampling method. According to rounding, the number of orders that need to be re-inspected is 1019.

[0086] See also Figure 4 ,The specific steps of detection management are: obtain the distribution map of temperature sensors in the mine from the database; install several temperature sensors according to the distribution map of temperature sensors; collect the real-time temperature of various places in the mine through temperature sensors; divide the environment in the mine into six areas; among which, the six areas include: explosive area, dusty area, humid area, high temperature area, corrosive gas area, and high-intensity vibration area;

[0087] When the temperature sensor installation location in the mine includes any Class 1-2 area, the corresponding temperature sensor is marked as Class A;

[0088] When the installation location of the temperature sensor in the mine includes any category 3-4 area, the corresponding temperature sensor is marked as level B;

[0089] When the installation location of the temperature sensor in the mine includes any category 5-6 area, the corresponding temperature sensor shall be marked as level C; where level A < level B < level C;

[0090] Determine in sequence in a set order whether the temperature sensors installed in the corresponding areas in different levels are responded to by the corresponding responders;

[0091] If yes, the temperature sensing effect of the temperature sensor installed in the corresponding area is not weakened;

[0092] If no, it is determined whether an early warning message can be issued when the temperature exceeds the standard range of the ventilation system; if yes, the temperature sensing effect of the temperature sensor installed in the corresponding area is not weakened; if no, the temperature sensing effect of the temperature sensor installed in the corresponding area is weakened;

[0093] According to the actual use environment of different mining areas, formulate the qualification standards for corresponding regional products, and optimize product design according to the qualification standards; among which, optimizing product design includes: using corrosion-resistant materials, enhancing sealing performance, strengthening quality control, improving production processes, and strengthening employee training.

[0094] For example, the temperature sensors required by the corresponding mine are randomly selected from the production order, and three temperature sensors are installed according to the distribution map of the temperature sensors in the mine and marked as a1, a2, and a3 respectively; among them, if the installation position of a1 is in the explosive area and the dusty area, the corresponding a1 sensor is marked as level A; if the installation position of a2 is in the high temperature area, the corrosive gas area, and the high-intensity vibration area, the corresponding a2 sensor is marked as level B; if the installation position of a3 is in the explosive area, the dusty area, the humid area, the corrosive gas area, and the high-intensity vibration area, the corresponding a3 sensor is marked as level C;

[0095] Set the judgment order to level A, level B, level C;

[0096] The temperature sensor data installed on the mine channel is retrieved; it is determined that the channel lights can be automatically turned on after the temperature changes in the level A and B areas, and the temperature sensing effect of the temperature sensor has not been weakened; while the channel lights can be automatically turned on after the temperature changes in the level C area, and the temperature sensing effect of the temperature sensor has been weakened, and the error correction of the a3 sensor corresponding to the level C is performed;

[0097] The temperature data of temperature sensors a1 and a2 installed at the air compressor outlet and the air bag outlet are obtained from the database, and the temperature threshold is set to 40°C; at this time, the temperature of the air compressor outlet and the air bag outlet is 41°C; the real-time temperature data of temperature sensor a1 is 41°C, and the real-time temperature data of temperature sensor a2 is 39.8°C, and temperature sensor a1 issues a warning message; temperature sensor a2 fails to issue a warning message; the temperature sensing effect of temperature sensor a1 has not weakened, and the a1 sensor corresponding to level A does not need to be corrected for error; the temperature sensing effect of temperature sensor a2 has weakened, and the a2 sensor corresponding to level B does not need to be corrected for error.

[0098] For example, formulate qualification standards for products in corresponding regions, that is, further tighten the original qualification standards, such as improving the assembly accuracy of temperature sensors by using corrosion-resistant materials, enhancing sealing performance, strengthening quality control, improving production processes, and strengthening employee training, thereby reducing the errors caused by assembly and narrowing the gaps between several accessories.

[0099] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0100] The working principle of the present invention is as follows: inputting a historical failure rate data set into a probability prediction model to obtain the failure rate; determining the number of pre-production orders based on the failure rate; wherein the probability prediction model is constructed based on an artificial intelligence model; and dividing the failure rate into levels, and determining the proportion of the pre-production orders that need to be re-inspected based on the divided levels; obtaining the customer's actual use environment of the temperature sensor, dividing the actual use environment into partitions, and using a digital twin model to perform environmental simulation; determining whether the temperature sensing effect of the temperature sensor is weakened due to environmental reasons; if yes, performing error correction; if not, continuing to determine.

[0101] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A production management system for mine temperature sensors, characterized in that: include: Data storage module, production management module, inspection management module, system management module, and safety and environmental management module; Data storage module: used to store the order information of mining temperature sensors and historical unqualified rate data sets; the order information includes: supplier information, procurement information and raw materials required for the order; Production management module: inputting the historical non-conforming rate data set into the probability prediction model to obtain the non-conforming rate; determining the number of pre-production orders based on the non-conforming rate; wherein the probability prediction model is built based on the artificial intelligence model; and, The non-conforming rate is divided into grades, and the proportion of pre-production orders that need to be re-inspected is determined according to the grades; Detection management module: obtain the customer's actual use environment of the temperature sensor, divide the actual use environment into zones, and use the digital twin model to simulate the environment; determine whether the temperature sensor's temperature sensing effect is weakened due to environmental reasons; if yes, perform error correction; if no, continue to judge; System management module: Create three major units to set up the system and complete the production management process.

2. A production management system for a mine temperature sensor according to claim 1, characterized in that: Also includes: Safety and environmental management module: used to regulate the production process to comply with safety standards and environmental requirements; Monitor potential safety hazards and environmental pollution issues in the production process, and regularly train and publicize safety and environmental protection knowledge.

3. The production management system of a mine temperature sensor according to claim 1, characterized in that: The step of inputting the historical failure rate data set into the probability prediction model to obtain the failure rate includes: Acquire a historical failure rate data set from a database, and mark the historical failure rate data set as an abnormal identification sequence; Retrieve the probability prediction model, input the anomaly recognition sequence into the probability prediction model, and obtain the anomaly label; match the corresponding anomaly type according to the anomaly label; match the corresponding unqualified rate according to the anomaly label and mark the unqualified rate as NP; wherein the anomaly label is set to a positive integer.

4. The production management system of a mine temperature sensor according to claim 1, characterized in that: The probability prediction model is constructed based on an artificial intelligence model, including: Acquire standard training data; wherein the standard training data includes standard input data consistent with the content attributes of the abnormal recognition sequence, and standard output data consistent with the content attributes of the abnormal label; The artificial intelligence model is trained using standard training data, and the trained artificial intelligence model is marked as an abnormal prediction model; wherein the artificial intelligence model includes a convolutional neural network model or a long short-term memory neural network model.

5. The production management system of a mine temperature sensor according to claim 1, characterized in that: Determining the number of pre-production orders according to the non-conforming rate includes: Retrieve the failure rate NP, obtain the number of user orders from the database and mark it as DT, and mark the safety factor as α; where 0<α≤1; The number of pre-production orders is calculated using the formula DS = LC × (1 + NP) × α; The safety margin is calculated using the formula AY=DT-DS.

6. The production management system of a mine temperature sensor according to claim 1, characterized in that: The ratio of the number of pre-production orders that need to be re-inspected according to the divided levels includes: Retrieve the non-conforming rate NP, set the non-conforming rate thresholds of three levels as T1, T2, and T3 respectively; mark the number of orders that need to be re-inspected as ɡ and β; where 0≤T1<T2≤0.05<T3<1; 0≤NP≤1; 0<ɡ<β<1; When 0≤NP≤T1, there is no need to recheck the number of pre-production orders; When T1<NP≤T2, recheck ɡ% of the pre-production orders by simple random sampling method; When T2<NP, recheck β% of the pre-produced orders by simple random sampling method; When NP=T3, all pre-production orders need to be rechecked.

7. The production management system of a mine temperature sensor according to claim 1, characterized in that: The actual use environment is partitioned, including: Obtain the distribution map of temperature sensors in the mine from the database; install several temperature sensors according to the distribution map of temperature sensors; collect real-time temperatures at various locations in the mine through temperature sensors; divide the environment in the mine into six types of areas; the six types of areas include: explosive areas, dusty areas, humid areas, high-temperature areas, areas containing corrosive gases, and areas with high-intensity vibrations; When the temperature sensor installation location in the mine includes any Class 1-2 area, the corresponding temperature sensor is marked as Class A; When the installation location of the temperature sensor in the mine includes any category 3-4 area, the corresponding temperature sensor is marked as level B; When the installation location of the temperature sensor in the mine includes any category 5-6 area, the corresponding temperature sensor is marked as level C; Among them, level A<level B<level C.

8. The production management system of a mine temperature sensor according to claim 1, characterized in that: The determining whether the temperature sensing effect of the temperature sensor is weakened due to environmental reasons includes: Determine in sequence in a set order whether the temperature sensors installed in the corresponding areas in different levels are responded to by the corresponding responders; If yes, the temperature sensing effect of the temperature sensor installed in the corresponding area is not weakened; If no, determine whether an early warning message can be issued when the temperature exceeds the standard range of the ventilation system; if yes, the temperature sensing effect of the temperature sensor installed in the corresponding area has not been weakened; if no, the temperature sensing effect of the temperature sensor installed in the corresponding area has been weakened.

9. The production management system of a mine temperature sensor according to claim 1, characterized in that: The error correction comprises: According to the actual use environment of different mining areas, formulate the qualification standards for corresponding regional products, and optimize product design according to the qualification standards; among which, optimizing product design includes: using corrosion-resistant materials, enhancing sealing performance, strengthening quality control, improving production processes, and strengthening employee training.

10. The production management system of a mine temperature sensor according to claim 1, characterized in that: The creation of three major units to set up the system completes the production management process, including: Authority allocation unit: including allocating authority to staff members, and tracking and auditing employees’ operation records; Product management unit: records the product's warehousing information, outbound information, and inventory information after production; Coding management unit: Encode the products entering the warehouse.

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